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https://github.com/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 🔥 想要系统的学习推荐算法的小伙伴，欢迎 Star 或者 Fork 到自己仓库进行学习🚀 有任何疑问欢迎提 Issues，也可加文末的联系方式向我询问！
https://github.com/jc-LeeHub/Recommend-System-tf2.0
afm autoint ccpm dcn deepfm dien dsin ffm fgcnn fm fnn pnn tf2 widedeep
Last synced: 3 months ago
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原理解析及代码实战，推荐算法也可以很简单 🔥 想要系统的学习推荐算法的小伙伴，欢迎 Star 或者 Fork 到自己仓库进行学习🚀 有任何疑问欢迎提 Issues，也可加文末的联系方式向我询问！
- Host: GitHub
- URL: https://github.com/jc-LeeHub/Recommend-System-tf2.0
- Owner: jc-LeeHub
- Created: 2020-12-11T09:42:19.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-19T09:34:18.000Z (almost 3 years ago)
- Last Synced: 2023-11-07T15:43:48.912Z (about 1 year ago)
- Topics: afm, autoint, ccpm, dcn, deepfm, dien, dsin, ffm, fgcnn, fm, fnn, pnn, tf2, widedeep
- Language: Python
- Homepage: https://github.com/jc-LeeHub/
- Size: 2.47 MB
- Stars: 569
- Watchers: 6
- Forks: 198
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-list - Recommend-System-TF2.0 - 经典推荐算法的原理解析及代码实现。 (Recommendation, Advertisement & Ranking / Others)
README
# 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](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/FM) | [ICDM 2010] [Fast Context-aware Recommendationswith Factorization Machines](https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle_et_al2011-Context_Aware.pdf) |
| [CCPM](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/CCPM) | [CIKM 2015] [A Convolutional Click Prediction Model](http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf) |
| [FFM](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/FFM) | [RecSys 2016] [Field-aware Factorization Machines for CTR Prediction](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) |
| [FNN](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/FNN) | [ECIR 2016] [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf) |
| [PNN](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/PNN) | [ICDM 2016] [Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf) |
| [Wide & Deep](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/WideDeep) | [DLRS 2016] [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) |
| [Deep Crossing](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/DeepCrossing) | [KDD 2016] [Deep Crossing: Web-Scale Modeling withoutManually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
| [DeepFM](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/DeepFM) | [IJCAI 2017] [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf) |
| [Deep & Cross Network](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/DCN) | [ADKDD 2017] [Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) |
| [AFM](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/AFM) | [IJCAI 2017] [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) |
| [NFM](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/NFM) | [SIGIR 2017] [Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) |
| Piece-wise Linear Model | [arxiv 2017] [Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/abs/1704.05194) |
| [xDeepFM](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/xDeepFM) | [KDD 2018] [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf) |
| [DIN](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/DIN) | [KDD 2018] [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)
| [MMoE](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/MMOE) | [KDD 2018] [Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/pdf/10.1145/3219819.3220007) ||
| FwFM | [WWW 2018] [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf) |
| [AutoInt](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/AutoInt) | [CIKM 2019] [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) |
| DIEN | [AAAI 2019] [Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) |
| ONN | [arxiv 2019] [Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf) |
| [FGCNN](https://github.com/jc-LeeHub/Recommend-System-tf2.0/tree/master/FGCNN) | [WWW 2019] [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction ](https://arxiv.org/pdf/1904.04447) |
| DSIN | [IJCAI 2019] [Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482) |
| FiBiNET| [RecSys 2019] [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)|
| FLEN | [arxiv 2019] [FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf) |
| DCN V2 | [arxiv 2020] [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535) |
## 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](https://www.zhihu.com/column/c_1330637706267734016)
- 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**](https://tf.wiki/zh_hans/basic/models.html)
## 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](https://www.zhihu.com/people/yu-yi-chu-shi)
- CSDN: [予以初始](https://blog.csdn.net/weixin_45658131?spm=1000.2115.3001.5343)
- Website: [HomePage](https://jc-leehub.github.io/)
- E-mail: [email protected]
- wechat ID: Liii00061333
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