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.
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.
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*-
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
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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
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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
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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
-
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
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Self-supervised Graph Learning for Recommendation [PDF]
Wu, Jiancan, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie.
SIGIR 2021
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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
-
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
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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
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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
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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
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Bundle Recommendation with Graph Convolutional Networks [PDF]
Chang, Jianxin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li
SIGIR 2020
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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
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Memory Augmented Graph Neural Networks for Sequential Recommendation [PDF]
Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates
AAAI 2020
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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
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Inductive Matrix Completion Based on Graph Neural Networks [PDF]
Muhan Zhang, Yixin Chen
ICLR 2020
-
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
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A Neural Influence Diffusion Model for Social Recommendation [PDF]
Wu, Le, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang
SIGIR 2019
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Graph Neural Networks for Social Recommendation [PDF]
Fan, Wenqi, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin.
WWW 2019
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Knowledge Graph Convolutional Networks for Recommender Systems [PDF]
Wang, Hongwei, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo.
WWW 2019
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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
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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
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STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [PDF]
Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King
IJCAI 2019
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Binarized Collaborative Filtering with Distilling Graph Convolutional Networks [PDF]
Haoyu Wang, Defu Lian, Yong Ge
IJCAI 2019
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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
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Session-based Recommendation with Graph Neural Networks. [PDF]
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
AAAI 2019
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Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. [PDF]
Jin Shang, Mingxuan Sun
AAAI 2019
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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
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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
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KGAT: Knowledge Graph Attention Network for Recommendation [PDF]
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua
KDD 2019
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Knowledge Graph Convolutional Networks for Recommender Systems [PDF]
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo
WWW 2019
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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
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Graph Neural Networks for Social Recommendation [PDF]
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin
WWW 2019
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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
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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks [PDF]
Federico Monti, Michael M. Bronstein, Xavier Bresson
NIPS 2017
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Graph Convolutional Matrix Completion [PDF]
Rianne van den Berg, Thomas N. Kipf, Max Welling
arXiv 2017
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Self-supervised on Graphs: Contrastive, Generative, or Predictive. [PDF]
Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan.Z.Li
arXiv 2021
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Contrastive Learning for Recommender System [PDF]
Zhuang Liu, Yunpu Ma, Yuanxin Ouyang, Zhang Xiong
arXiv 2021
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Contrastive Learning for Sequential Recommendation [PDF]
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui
arXiv 2021
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Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [PDF]
Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, Caiming Xiong
arXiv 2021
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Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation [PDF]
Tong, Xiaohai, Pengfei Wang, Chenliang Li, Long Xia, and Shaozhang Niu
IJCAT 2021
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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
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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
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Self-supervised Learning for Large-scale Item Recommendations [PDF]
Yao, Tiansheng, et al.
arXiv 2020