/GNN4Rec-Papers

Recent papers on Graph Neural Networks-based Recommender System.

Must-read Papers on GNN-based-Recommender System

GNN: Graph Neural Network.

Contributed by Zihan Liao (AIDA, East China Normal University).

Content

1. Survey
2. General Recommendation
 2.1 User-Item Bipartite Graph Models
 2.2 Social Graph Enhanced Models
 2.3 Knowledge Graph Enhanced Models
 2.4 Others
3. Sequential Recommendation
 3.1 RNN-based Models
 3.2 Attention-based Models
 3.3 Dynamic GNN-based Models
4. Related Technology
 4.1 Matrix Completion
 4.2 Random Walk
 4.3 Self-Supervised Learning
 4.4 Knowledge Distillation
 4.5 Hypergraph
 4.6 Variational Inference
5. Application-Scenarios
 5.1 Social Recommendation
 5.2 Package Recommendation
 5.3 Attack in Recommendation
 5.4 Diversified Recommendation
 5.5 Hashtag Recommendation
 5.6 CTR Prediction

Survey

  1. Graph Neural Networks in Recommender Systems: A Survey. arXiv, 2020. paper

    Shiwen Wu, Wentao Zhang, Fei Sun, Bin Cui.

General Recommendation

User-Item Bipartite Graph Models

  1. Self-supervised Graph Learning for Recommendation. SIGIR, 2021. paper code

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

  2. DGCN: Diversified Recommendation with Graph Convolutional Networks. WWW, 2021. paper code

    Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, Yong Li.

  3. Graph Embedding for Recommendation against Attribute Inference Attacks. WWW, 2021. paper

    Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang.

  4. HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. WWW, 2021. paper code

    Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, Maksims Volkovs.

  5. Multi-Component Graph Convolutional Collaborative Filtering. AAAI, 2020. paper code

    Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li.

  6. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. AAAI, 2020. paper code

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

  7. GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems. CIKM, 2020. paper

    Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates.

  8. Inductive Matrix Completion Based on Graph Neural Networks. ICLR, 2020. paper code

    Muhan Zhang, Yixin Chen.

  9. MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks. KDD, 2020. paper

    Carl Yang, Aditya Pal, Andrew Zhai, Nikil Pancha, Jiawei Han, Charles Rosenberg, Jure Leskovec.

  10. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. KDD, 2020. paper code

    Wenhui Yu, Zheng Qin.

  11. An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph. KDD, 2020. paper code

    Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola.

  12. M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems. KDD, 2020. paper code

    Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, Xiao-Ming Wu.

  13. Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps. KDD, 2020. paper code

    Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, Suhang Wang.

  14. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR, 2020. paper code

    Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, Meng Wang

  15. Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation. WSDM, 2020. paper code

    Yuan Zhang, Xiaoran Xu, Hanning Zhou, Yan Zhang

  16. Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network. EMNLP/IJCNLP, 2019. paper code

    Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie.

  17. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. IJCAI, 2019. paper

    Haoyu Wang, Defu Lian, Yong Ge.

  18. MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. MM, 2019. paper code

    Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua.

  19. A Novel Enhanced Collaborative Autoencoder with Knowledge Distillation for Top-N Recommender Systems. Neurocomputing, 2018. paper

    Yiteng Pan, Fazhi He, Haiping Yu.

Social Graph Enhanced Models

  1. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. WWW, 2021. paper code

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

  2. DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation. TKDE, 2020. paper code

    Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang.

  3. Modelling High-Order Social Relations for Item Recommendation. TKDE, 2020. paper

    Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang.

  4. paper2repo: GitHub Repository Recommendation for Academic Papers. WWW, 2020. paper code

    Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek F. Abdelzaher.

  5. Graph Enhanced Representation Learning for News Recommendation. WWW, 2020. paper

    Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang.

  6. Multi-Graph Convolution Collaborative Filtering. ICDM, 2019. paper

    Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He.

Knowledge Graph Enhanced Models

  1. Drug Package Recommendation via Interaction-aware Graph Induction. WWW, 2021. paper

    Zhi Zheng, Chao Wang, Dazhong Shen, Baoxing Huai, Tongzhu Liu, Enhong Chen.

  2. Multi-behavior Recommendation with Graph Convolutional Networks. SIGIR, 2020. paper

    Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, Yong Li.

  3. A2-GCN: An Attribute-aware Attentive GCN Model for Recommendation. TKDE, 2020. paper code

    Fan Liu; Zhiyong Cheng; Lei Zhu; Chenghao Liu; Liqiang Nie.

  4. paper2repo: GitHub Repository Recommendation for Academic Papers. WWW, 2020. paper code

    Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek F. Abdelzaher.

  5. Graph Enhanced Representation Learning for News Recommendation. WWW, 2020. paper

    Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang.

  6. Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network. CIKM, 2019. paper

    Mengmeng Li, Tian Gan, Meng Liu, Zhiyong Cheng, Jianhua Yin, Liqiang Nie.

  7. Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks. CIKM, 2019. paper

    Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang.

  8. Multi-Graph Convolution Collaborative Filtering. ICDM, 2019. paper

    Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He.

Others

  1. Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction. CIKM, 2019. paper code

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

Sequential Recommendation

RNN-based Models

  1. Dynamic Memory based Attention Network for Sequential Recommendation. AAAI, 2021. paper

    Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu.

  2. Handling Information Loss of Graph Neural Networks for Session-based Recommendation. KDD, 2020. paper code

    Tianwen Chen, Raymond Chi-Wing Wong.

  3. Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. KDD, 2020. paper code

    Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla.

  4. Session-Based Recommendation with Graph Neural Networks. AAAI, 2019. paper code

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

  5. Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks. CIKM, 2019. paper code

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

Attention-based Models

  1. Dynamic Memory based Attention Network for Sequential Recommendation. AAAI, 2021. paper

    Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu.

  2. Sequence-aware Heterogeneous Graph Neural Collaborative Filtering. SDM, 2021. paper

    Chen Li, Linmei Hu, Chuan Shi, Guojie Song, Yuanfu Lu.

  3. Memory Augmented Graph Neural Networks for Sequential Recommendation. AAAI, 2020. paper

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

Dynamic GNN-based Models

Related Technology

Matrix Completion

  1. Inductive Matrix Completion Based on Graph Neural Networks. ICLR, 2020. paper code

    Muhan Zhang, Yixin Chen.

Random Walk

  1. An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph. KDD, 2020. paper code

    Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola.

  2. Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation. WSDM, 2020. paper code

    Yuan Zhang, Xiaoran Xu, Hanning Zhou, Yan Zhang

Self-Supervised Learning

  1. Self-Supervised Learning on Graphs: Deep Insights and New Directions. arXiv, 2021. paper

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

  2. Self-supervised Graph Learning for Recommendation. SIGIR, 2021. paper code

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

  3. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. WWW, 2021. paper code

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

Knowledge Distillation

  1. GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems. CIKM, 2020. paper

    Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates.

  2. Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation. WSDM, 2020. paper code

    Yuan Zhang, Xiaoran Xu, Hanning Zhou, Yan Zhang

  3. Knowledge Distillation via Instance Relationship Graph. CVPR, 2019. paper

    Yufan Liu, Jiajiong Cao, Bing Li, Chunfeng Yuan, Weiming Hu, Yangxi Li, Yunqiang Duan.

  4. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. IJCAI, 2019. paper

    Haoyu Wang, Defu Lian, Yong Ge.

Hypergraph

  1. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. WWW, 2021. paper code

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

Variational Inference

  1. Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks. CIKM, 2019. paper

    Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang.

Application Scenarios

Social Recommendation

  1. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. WWW, 2021. paper code

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

Package Recommendation

  1. Drug Package Recommendation via Interaction-aware Graph Induction. WWW, 2021. paper

    Zhi Zheng, Chao Wang, Dazhong Shen, Baoxing Huai, Tongzhu Liu, Enhong Chen.

Attack in Recommendation

  1. Graph Embedding for Recommendation against Attribute Inference Attacks. WWW, 2021. paper

    Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang.

Diversified Recommendation

  1. DGCN: Diversified Recommendation with Graph Convolutional Networks. WWW, 2021. paper code

    Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, Yong Li.

Hashtag Recommendation

  1. Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network. CIKM, 2019. paper

    Mengmeng Li, Tian Gan, Meng Liu, Zhiyong Cheng, Jianhua Yin, Liqiang Nie.

CTR Prediction

  1. Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction. CIKM, 2019. paper code

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