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All Projects → juyongjiang → awesome-graph-self-supervised-learning-based-recommendation

juyongjiang / awesome-graph-self-supervised-learning-based-recommendation

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A curated list of awesome graph & self-supervised-learning-based recommendation.

Labels

machine-learningdeep-learningrecommendation-systemgraph-neural-networksself-supervised-learningknowledge-graph-for-recommendationcontrastive-learninggraph-based-recommendation

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Awesome Graph & SSL-based Recommendation

Awesome GitHub stars GitHub forks visitors

A curated list of awesome Graph & Self-Supervised-Learning-based Recommendation resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, awesome-architecture-search, awesome-self-supervised-learning, awesome-self-supervised-learning-for-graphs, and GNNPapers.

Introduction

With the explosive growth of the amount of information on the Internet, recommender systems play a crucial role to alleviate the problem of information overload. The graph-based recommendation is a promising method to capture users' dynamic preferences and complex transitions of items in realistic scenarios. Besides, to eliminate the problem of label scarcity, self-supervised learning (SSL) has been attracted a lot of research attention and achieved remarkable successes in various fields, e.g. visual, natural language processing, and robotics. However, the development of SSL in the recommendation domain is still at a nascent stage. Moreover, due to the complexity of users' dynamic interest patterns and item's various attributes, constructing applicative self-supervision signals can extract more meaningful user behavior patterns and further encode the user and item representations effectively. This vibrant research direction is termed self-supervised learning-based recommendation. This repository provides you with a curated list of awesome Graph & Self-Supervised-Learning-based Recommendation resources.

Contributing

Please, feel free to send pull requests to add more resources!

Markdown Format:

- Paper Name. [[PDF]](link) [[Code]](link)
  Author 1, Author 2, and Author 3. 
  *Conference Year*

Table of Contents

Graph-based Recommendation

Surveys

  • Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions [PDF]

    Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, Yong Li

    TOIS 2021

  • Graph Learning based Recommender Systems: A Review [PDF]

    Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu

    IJCAI 2021

  • Graph Neural Networks in Recommender Systems: A Survey [PDF]

    Shiwen Wu, Fei Sun, Wentao Zhang, Bin Cui

    arXiv 2020

  • A Survey on Knowledge Graph-Based Recommender Systems [PDF]

    Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He

    arXiv 2020

  • A Comprehensive Survey on Graph Neural Networks [PDF]

    Wu, Zonghan, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip

    IEEE Transactions on Neural Networks and Learning Systems 2020

2021

  • HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation [PDF]

    Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Yunan Zhao, Bing Han, Lin Zheng, Kaixin Gao, Xiaobo Guo

    arXiv 2021

  • Self-Supervised Graph Co-Training for Session-based Recommendation [PDF]

    Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, Lizhen Cui

    CIKM 2021

  • Self-supervised Graph Learning for Recommendation [PDF]

    Wu, Jiancan, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie.

    SIGIR 2021

  • Sequential Recommendation with Graph Neural Networks [PDF]

    Chang, Jianxin, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li.

    SIGIR 2021

  • Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation [PDF]

    Xia, Xin, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang

    AAAI 2021

  • Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [PDF]

    Yu, Junliang, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang

    WWW 2021

2020

  • Handling Information Loss of Graph Neural Networks for Session-based Recommendation [PDF]

    Chen, Tianwen, and Raymond Chi-Wing Wong

    KDD 2020

  • Global context enhanced graph neural networks for session-based recommendation [PDF]

    Wang, Ziyang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu

    SIGIR 2020

  • Target Attentive Graph Neural Networks for Session-based Recommendation [PDF]

    Yu, Feng, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan

    SIGIR 2020

  • Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction [PDF]

    Wang, Wen, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, and Hongyuan Zha

    WWW 2020

  • Global Context Enhanced Graph Neural Networks for Session-based Recommendation [PDF]

    Wang, Ziyang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu

    SIGIR 2020

  • Handling Information Loss of Graph Neural Networks for Session-based Recommendation [PDF]

    Chen, Tianwen, and Raymond Chi-Wing Wong

    KDD 2020

  • Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation [PDF]

    Chang, Buru, Gwanghoon Jang, Seoyoon Kim, and Jaewoo Kang

    CIKM 2020

  • GAME: Learning Graphical and Attentive Multi-View Embeddings for Occasional Group Recommendation [PDF]

    He, Zhixiang, Chi-Yin Chow, and Jia-Dong Zhang

    SIGIR 2020

  • Bundle Recommendation with Graph Convolutional Networks [PDF]

    Chang, Jianxin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li

    SIGIR 2020

  • LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation [PDF]

    He, Xiangnan, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang

    SIGIR 2020

  • Memory Augmented Graph Neural Networks for Sequential Recommendation [PDF]

    Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates

    AAAI 2020

  • Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [PDF]

    Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang

    AAAI 2020

  • Inductive Matrix Completion Based on Graph Neural Networks [PDF]

    Muhan Zhang, Yixin Chen

    ICLR 2020

2019

  • Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks [PDF]

    Qiu, Ruihong, Jingjing Li, Zi Huang, and Hongzhi Yin

    CIKM 2019

  • Graph Contextualized Self-Attention Network for Session-based Recommendation [PDF]

    Xu, Chengfeng, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou

    IJCAI 2019

  • Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks [PDF]

    Qiu, Ruihong, Jingjing Li, Zi Huang, and Hongzhi Yin

    CIKM 2019

  • A Neural Influence Diffusion Model for Social Recommendation [PDF]

    Wu, Le, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang

    SIGIR 2019

  • Graph Neural Networks for Social Recommendation [PDF]

    Fan, Wenqi, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin.

    WWW 2019

  • Knowledge Graph Convolutional Networks for Recommender Systems [PDF]

    Wang, Hongwei, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo.

    WWW 2019

  • KGAT: Knowledge Graph Attention Network for Recommendation [PDF]

    Wang, Xiang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua

    KDD 2019

  • Graph contextualized self-attention network for session-based recommendation [PDF]

    Xu, Chengfeng, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou

    IJCAI 2019

  • Session-based social recommendation via dynamic graph attention networks [PDF]

    Song, Weiping, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang

    WSDM 2019

  • Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction [PDF]

    Li, Zekun, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang

    WWW 2019

  • Neural Graph Collaborative Filtering [PDF]

    Wang, Xiang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua

    SIGIR 2019

  • STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [PDF]

    Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King

    IJCAI 2019

  • Binarized Collaborative Filtering with Distilling Graph Convolutional Networks [PDF]

    Haoyu Wang, Defu Lian, Yong Ge

    IJCAI 2019

  • Graph Contextualized Self-Attention Network for Session-based Recommendation [PDF]

    Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou

    IJCAI 2019

  • Session-based Recommendation with Graph Neural Networks. [PDF]

    Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan

    AAAI 2019

  • Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. [PDF]

    Jin Shang, Mingxuan Sun

    AAAI 2019

  • Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [PDF]

    Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang

    KDD 2019

  • Exact-K Recommendation via Maximal Clique Optimization [PDF]

    Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu

    KDD 2019

  • KGAT: Knowledge Graph Attention Network for Recommendation [PDF]

    Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua

    KDD 2019

  • Knowledge Graph Convolutional Networks for Recommender Systems [PDF]

    Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo

    WWW 2019

  • Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems [PDF]

    Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen

    WWW 2019

  • Graph Neural Networks for Social Recommendation [PDF]

    Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin

    WWW 2019

2018-

  • Graph Convolutional Neural Networks for Web-Scale Recommender Systems [PDF]

    Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

    KDD 2018

  • Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks [PDF]

    Federico Monti, Michael M. Bronstein, Xavier Bresson

    NIPS 2017

  • Graph Convolutional Matrix Completion [PDF]

    Rianne van den Berg, Thomas N. Kipf, Max Welling

    arXiv 2017

SSL-based-Recommendation

2021

  • Self-supervised on Graphs: Contrastive, Generative, or Predictive. [PDF]

    Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan.Z.Li

    arXiv 2021

  • Contrastive Learning for Recommender System [PDF]

    Zhuang Liu, Yunpu Ma, Yuanxin Ouyang, Zhang Xiong

    arXiv 2021

  • Contrastive Learning for Sequential Recommendation [PDF]

    Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui

    arXiv 2021

  • Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [PDF]

    Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, Caiming Xiong

    arXiv 2021

  • Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation [PDF]

    Tong, Xiaohai, Pengfei Wang, Chenliang Li, Long Xia, and Shaozhang Niu

    IJCAT 2021

2020

  • Self-supervised learning on graphs: Deep insights and new direction [PDF]

    Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang

    arXiv 2020

  • S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [PDF]

    Zhou, Kun, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen.

    CIKM 2020

  • Self-supervised Learning for Large-scale Item Recommendations [PDF]

    Yao, Tiansheng, et al.

    arXiv 2020

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