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深度树模型实验

该项目优化和完善了git另外一个哥们写的实验室类型的的项目,优化了大数据量情况下在生产环境成功运行

代码结构

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
din_model.py: DIN网络搭建 prediction.py: 遍历树预测部分 dataset.py:数据生成迭代器 relative_album_caculate:专辑的相关专辑计算

算法模型

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 该过程全部在tdm.py中

首先运行sample_init.py 然后运行tdm.py

进度

100w用户,每个用户5个播放历史跑通

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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