【GCN-RS系列汇总】十三篇文章、十六个模型梳理图神经网络协同过滤模型（附代码实现）

整理了博客已有的GCN-RS的文章，持续更新中。
一、【基础知识：DeepWalk、Node2Vector、GCN】
二、【经典工作：NGCF、LightGCN、LR-GCCF】
三、【多行为推荐：MBGCN】
四、【多关系推荐：GHCF】
五、【对比学习SGL】
六、【Item-KNN + WMF = UltraGCN】
七、【也是KNN算法：How Powerful GCN RS】
八、【YouTubeNet、MF又一春：SimpleX】
九、【GCN的难负样本：MixGCF】
（附赠Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering （NIPS 2020））
十、【GCN+NRF：GraphRFI】
十一、【两种正则化：DGCN-HN】

以上文章基本都是开源的，个人复现了部分代码，整理在了一个框架内，开源地址：RS_Zoos

参考文献：

• Neural Graph Collaborative Filtering (SIGIR’19)
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (SIGIR 2020)
• Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (AAAI 2020)
• Multi-behavior Recommendation with Graph Convolutional Networks (SIGIR 2020)
• Graph Heterogeneous Multi-Relational Recommendation (AAAI 2021)
• Self-supervised Graph Learning for Recommendation (SIGIR 2021)
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (CIKM’21) FuxiCTR、BARS副产物
• SimpleX: A Simple and Strong Baseline for Collaborative Filtering (CIKM‘21) FuxiCTR、BARS副产物
• How Powerful is Graph Convolution for Recommendation? (CIKM‘21)
• Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering （NIPS 2020）
• MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems （KDD 2021）
• GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection (SIGIR’20)
• Deep GCN with Hybrid Normalization for Accurate and Diverse Recommendation (DLP-KDD‘21)