Merge branch 'main' of https://github.com/WING-NUS/wing-website

content/publication/10-1145-3336191-3371769/cite.bib

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+@inproceedings{10.1145/3336191.3371769,
+ abstract = {Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation"Action" Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.},
+ address = {New York, NY, USA},
+ author = {Lei, Wenqiang and He, Xiangnan and Miao, Yisong and Wu, Qingyun and Hong, Richang and Kan, Min-Yen and Chua, Tat-Seng},
+ booktitle = {Proceedings of the 13th International Conference on Web Search and Data Mining},
+ doi = {10.1145/3336191.3371769},
+ isbn = {9781450368223},
+ keywords = {conversational recommendation, dialogue system, interactive recommendation, recommender system},
+ location = {Houston, TX, USA},
+ numpages = {9},
+ pages = {304–312},
+ publisher = {Association for Computing Machinery},
+ series = {WSDM '20},
+ title = {Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems},
+ url = {https://doi.org/10.1145/3336191.3371769},
+ year = {2020}
+}

content/publication/10-1145-3336191-3371769/index.md

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+---
+title: 'Estimation-Action-Reflection: Towards Deep Interaction Between Conversational
+  and Recommender Systems'
+authors:
+- Wenqiang Lei
+- Xiangnan He
+- yisong
+- Qingyun Wu
+- Richang Hong
+- min
+- Tat-Seng Chua
+date: '2020-01-01'
+publishDate: '2024-07-23T15:15:04.606218Z'
+publication_types:
+- paper-conference
+publication: '*Proceedings of the 13th International Conference on Web Search and
+  Data Mining*'
+doi: 10.1145/3336191.3371769
+abstract: "Recommender systems are embracing conversational technologies to obtain
+  user preferences dynamically, and to overcome inherent limitations of their static
+  models. A successful Conversational Recommender System (CRS) requires proper handling
+  of interactions between conversation and recommendation. We argue that three fundamental
+  problems need to be solved: 1) what questions to ask regarding item attributes,
+  2) when to recommend items, and 3) how to adapt to the users' online feedback. To
+  the best of our knowledge, there lacks a unified framework that addresses these
+  problems. In this work, we fill this missing interaction framework gap by proposing
+  a new CRS framework named Estimation\\\"Action\\\" Reflection, or EAR, which consists
+  of three stages to better converse with users. (1) Estimation, which builds predictive
+  models to estimate user preference on both items and item attributes; (2) Action,
+  which learns a dialogue policy to determine whether to ask attributes or recommend
+  items, based on Estimation stage and conversation history; and (3) Reflection, which
+  updates the recommender model when a user rejects the recommendations made by the
+  Action stage. We present two conversation scenarios on binary and enumerated questions,
+  and conduct extensive experiments on two datasets from Yelp and LastFM, for each
+  scenario, respectively. Our experiments demonstrate significant improvements over
+  the state-of-the-art method CRM [32], corresponding to fewer conversation turns
+  and a higher level of recommendation hits."
+tags:
+- conversational recommendation
+- dialogue system
+- interactive recommendation
+- recommender system
+links:
+- name: URL
+  url: https://doi.org/10.1145/3336191.3371769
+---