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用pytorch 方法复现了二十多个经典的推荐算法论文,其中包含排序论文和推荐召回论文,并在demo里面选了一个召回模型和排序模型的运行示例。

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recome_wan

  • 1.本项目通过pytorch 框架大概复现了推荐系统相关的22个模型,其中包含多篇排序论文和多篇序列召回和图召回论文。
  • 2.本项目一共包含demo和recome_wan这两个文件夹。
  • 3.在demo里面分别选了一个召回模型和排序模型来作为示例,如果想调试其他的召回和排序模型,可以直接修改demo里面的rank_example.py文件或者recall_example.py的代码即可。
  • 4.在recome_wan这个文件夹里,一共包含datasets、models、trainer、utils这四个大的模块。
  • 5.其中datasets文件夹主要是数据类型和数据编码的处理,models里面包含了layers、rank_models和recall_models这三个文件夹。layers主要存放的是一些通用的层比如embedding层、Mlp层。 rank_models里存放的就是排序相关的模型,recall_models里存放的就是召回相关的模型。trainer主要是用来训练、验证、测试召回和排序的模型。utils包含一些关于召回模型的评价。 注:以下表格只展示了一些重要的模型,对一些简单的模型不做表格展示。
model paper
dcn DCN:Deep & Cross Network for Ad Click Predictions
deepfm DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
fibinet FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
mmoe MMOE:Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
line LINE: Large-scale Information Network Embedding
comirec COMIREC:Controllable Multi-Interest Framework for Recommendation
gru4rec GRU4REC:Session-based Recommendations with Recurrent Neural Networks
mind MIND:Multi-interest network with dynamic routing for recommendation at Tmall
youtubednn YOUTUBEDNN:Deep Neural Networks for YouTube Recommendations
lightgcn LightGcn: Simplifying and Powering Graph Convolution Network for Recommendation
deepwalk DeepWalk: Online Learning of Social Representations
node2vec Node2Vev: Scalable Feature Learning for Networks
din DIN:Deep Interest Network for Click-Through Rate Prediction
sdne SDNE:Structural Deep Network Embedding
graphsage GRAPHSAGE:Inductive Representation Learning on Large Graphs
eges EGES:Enhanced Graph Embedding with Side Information

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用pytorch 方法复现了二十多个经典的推荐算法论文,其中包含排序论文和推荐召回论文,并在demo里面选了一个召回模型和排序模型的运行示例。

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