jc-LeeHub/Recommend-System-tf2.0：原理解析及代码实战，推荐算法也可以很简单 🔥 想要系统的学

项目介绍

jc-LeeHub/Recommend-System-tf2.0 是一个托管在 GitHub 上的开源项目，主要使用 Python 编写，目前获得 728 个 Star、229 次 Fork。

仓库全名: jc-LeeHub/Recommend-System-tf2.0

README 摘要

Recommend-System-TF2.0 此仓库用于记录在学习推荐系统过程中的知识产出，主要是对经典推荐算法的原理解析及代码实现。算法包含但不仅限于下图中的算法，持续更新中... Models List Model | Paper | :----: | :------- | FM | [ICDM 2010] Fast Context-aware Recommendationswith Factorization Machines | CCPM | [CIKM 2015] A Convolutional Click Prediction Model | FFM | [RecSys 2016] Field-aware Factorization Machines for CTR Prediction | FNN | [ECIR 2016] Deep Learning over Multi-field Categorical…

完整内容见 GitHub 原始 README 或本页。

常见问题

Recommend-System-tf2.0 用什么语言开发？

主要使用 Python 编写，目前有 728 个 Star。

如何获取 Recommend-System-tf2.0 的源码？

可以直接克隆仓库：git clone https://github.com/jc-LeeHub/Recommend-System-tf2.0.git，默认分支为 master；安装与运行步骤见仓库 README。

Recommend-System-tf2.0 最近还在维护吗？

仓库最近一次更新为 2022年1月19日，当前有 8 个未关闭 Issue；Star 数为 728，可作为社区活跃度的参考。

此仓库用于记录在学习推荐系统过程中的知识产出，主要是对经典推荐算法的原理解析及代码实现。

算法包含但不仅限于下图中的算法，持续更新中…

Models List

Model	Paper
FM	[ICDM 2010] Fast Context-aware Recommendationswith Factorization Machines
CCPM	[CIKM 2015] A Convolutional Click Prediction Model
FFM	[RecSys 2016] Field-aware Factorization Machines for CTR Prediction
FNN	[ECIR 2016] Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
PNN	[ICDM 2016] Product-based neural networks for user response prediction
Wide & Deep	[DLRS 2016] Wide & Deep Learning for Recommender Systems
Deep Crossing	[KDD 2016] Deep Crossing: Web-Scale Modeling withoutManually Crafted Combinatorial Features
DeepFM	[IJCAI 2017] DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Deep & Cross Network	[ADKDD 2017] Deep & Cross Network for Ad Click Predictions
AFM	[IJCAI 2017] Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
NFM	[SIGIR 2017] Neural Factorization Machines for Sparse Predictive Analytics
Piece-wise Linear Model	[arxiv 2017] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
xDeepFM	[KDD 2018] xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
DIN	[KDD 2018] Deep Interest Network for Click-Through Rate Prediction
MMoE	[KDD 2018] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
FwFM	[WWW 2018] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
AutoInt	[CIKM 2019] AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
DIEN	[AAAI 2019] Deep Interest Evolution Network for Click-Through Rate Prediction
ONN	[arxiv 2019] Operation-aware Neural Networks for User Response Prediction
FGCNN	[WWW 2019] Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
DSIN	[IJCAI 2019] Deep Session Interest Network for Click-Through Rate Prediction
FiBiNET	[RecSys 2019] FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
FLEN	[arxiv 2019] FLEN: Leveraging Field for Scalable CTR Prediction
DCN V2	[arxiv 2020] DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

Introduction

原理结合代码食用更佳，掌握算法的最好方式就是用代码撸它
原理解析可参考知乎专栏推荐算法也可以很简单
代码实践参考本仓库即可，每个模型都有对应README.md，对模型原理、代码结构、实验结果进行了介绍

Tips: 该仓库使用的代码均为TF2.0，如果你不熟悉该框架，可参考文档简单粗暴的Tensorflow2.0

Citation

论文列表引用于浅梦，并作了相应补充. Weichen Shen.(2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr. 感谢整理！

About

知乎：予以初始
CSDN: 予以初始
Website: HomePage
E-mail: [email protected]
wechat ID: Liii00061333

Recommend-System-tf2.0

项目介绍

README 摘要

常见问题

关于

简要分析

语言占比

Release

贡献者

Models List

Introduction

Citation

About

评论

Recommend-System-tf2.0

项目介绍

README 摘要

常见问题

相关仓库

关于

简要分析

语言占比

Release

贡献者

Recommend-System-TF2.0

Models List

Introduction

Citation

About

评论