Skip to content

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!

Notifications You must be signed in to change notification settings

jc-LeeHub/Recommend-System-tf2.0

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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

About

About

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!

Topics

Resources

Stars

Watchers

Forks

Packages

No packages published

Languages