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awesome-models's Introduction

awesome-models

该仓库主要总结基于TensorFlow框架，实现的推荐模型。

  • TensorFlow基础部分：主要包括Tensorflow的基础op、变量初始化和激活函数的选择、常见loss及其应用、各种FeatureColumn的内部实现，以及底层EmbeddingLookup细节曝光。

  • 模型部分：主要聚焦于推荐系统的特征交互、序列推荐、召回模型、多目标模型的结构和融合排序**。 排序、召回等模块常用的模型结构均以Jupyter实现以探究不同模型算法在矩阵运算的具体细节。

Models List

Module Model Paper
特征交互 CAN CAN: Feature Co-Action for Click-Through Rate Prediction
.. DCN Deep & Cross Network for Ad Click Predictions
.. PNN Product-based Neural Networks for User Response Prediction
.. AFM Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
.. DIN Deep Interest Network for Click-Through Rate Prediction
.. FFM Field-aware Factorization Machines for CTR Prediction
.. DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
.. DeepCrossing Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
.. NCF Neural Collaborative Filtering
.. NFM Neural Factorization Machines for Sparse Predictive Analytics
多目标结构 MMoE Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
.. PLE Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
序列与召回 ComiRec Controllable Multi-Interest Framework for Recommendation
.. STAMP STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation
.. SASRec Self-Attentive Sequential Recommendation

目录

  • TensorFlow基础
  • 特征交互
  • 召回与序列推荐
  • 多目标结构

知乎与博客

awesome-models's People

Contributors

muzhi1920 avatar

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