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Deep neural network model based on the tree

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深度树模型实验


目录

  • 实验环境
  • 代码结构
  • 算法模型
  • 进度
  • 参考文献

实验环境

系统环境: ubuntu 18.04LTS
IDE:PyCharm 2018.3

实验数据集

代码结构

code-structure
文件说明
tdm.py: 代码入口,负责完整深度树模型的训练和测试
sample_init.py: 数据处理及生成程序,负责数据预处理及树样本的生成
construct_tree.py: 样本二叉树生成程序,负责树模型的生成
deep_network.py: DNN的实现程序,负责网络的搭建
prediction.py: 树节点预测及模型评测程序,负责模型预测及性能验证

算法模型

algorithm-structure
深度树算法流程(文献[1]):

  1. 构造随机二叉树
  2. 基于树模型生成样本
  3. 训练DNN模型直到收敛
  4. 基于DNN模型得到样本的Embedding,重新构造聚类二叉树
  5. 循环上述2~4过程

进度

完成功能测试,跑通模型
TODO: 改进性能,验证模型

参考文献

[1] Learning Tree-based Deep Model for Recommender Systems, Han Zhu, Xiang Li, Pengye Zhang, etc.
[2] Deep Interest Network for Click-Through Rate Prediction, Guorui Zhou, Chengru Song, Xiaoqiang Zhu, etc.
[3] Empirical Evaluation of Rectified Activations in Convolution Network, Bing Xu, Naiyan Wang, Tianqi Chen, etc.
[4] Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He, Xiangyu Zhang, Shaoqing Ren, etc.
[5] Distributed Representations of Words and Phrases and their Compositionality, Tomas Mikolov, Ilya Sutskever, Kai Chen, etc.

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Deep neural network model based on the tree

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