一、综述Survey

Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 2021.09.27

Graph Learning based Recommender Systems: A Review 2021.04.20

  • Shoujin Wang, Liang Hu, Yan Wang, et al. Graph Learning based Recommender Systems: A Review[C]. In AAAI 2021.

Recommender systems based on graph embedding techniques: A comprehensive review 2021.09.20

  • Yue Deng. Recommender systems based on graph embedding techniques: A comprehensive review[J]. In ArXiv 2021.

Graph Neural Networks in Recommender Systems: A Survey 2020.12.04

面向推荐系统的图卷积网络 2019.05.31

  • 葛尧, 陈松灿. 面向推荐系统的图卷积网络[J]. 软件学报, 2020.

图神经网络推荐研究进展 2019.08.30

PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems

  • Rex Ying, Ruining He, Kaifeng Chen, et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems[C]. In KDD 2018.

NGCF: Neural Graph Collaborative Filtering

  • Xiang Wang, Xiangnan He, Meng Wang, et al. Neural Graph Collaborative Filtering[C]. In SIGIR 2019.

DGCF: Disentangled Graph Collaborative Filtering

  • Xiang Wang, Hongye Jin, An Zhang, et al. Disentangled Graph Collaborative Filtering[C]. In SIGIR 2020.

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

SGL: Self-supervised Graph Learning for Recommendation

  • Jiancan Wu, Xiang Wang, Fuli Feng, et al. Self-supervised Graph Learning for Recommendation[C]. In SIGIR 2021.

NCL: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

  • Zihan Lin, Changxin Tian, Yupeng Hou, et al. Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning[C]. In WWW 2022.

    排序Ranking

    Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction

  • Zekun Li, Zeyu Cui, Shu Wu, et al. Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction[C]. In CIKM 2019.

PUP: Price-aware Recommendation with Graph Convolutional Networks

  • Yu Zheng, Chen Gao, Xiangnan He, et al. Price-aware Recommendation with Graph Convolutional Networks[C]. In ICDE 2020.

L0-SIGN: Detecting Beneficial Feature Interactions for Recommender Systems

  • Yixin Su, Rui Zhang, Sarah Erfani, et al. Detecting Beneficial Feature Interactions for Recommender Systems[C]. In AAAI 2021.

SHCF: Sequence-aware Heterogeneous Graph Neural Collaborative Filtering

  • Chen Li, Linmei Hu, Chuan Shi, et al. Sequence-aware Heterogeneous Graph Neural Collaborative Filtering[C]. In SDM 2021.

GCM: Graph Convolution Machine for Context-aware Recommender System 2021.05.11

  • Jiancan Wu, Xiangnan He, Xiang Wang, et al. Graph Convolution Machine for Context-aware Recommender System[J]. In Frontiers of Computer Science 2022.

    重排Re-ranking

    IRGPR: Personalized Re-ranking with Item Relationships for E-commerce

  • Weiwen Liu, Qing Liu, Ruiming Tang, et al. Personalized Re-ranking with Item Relationships for E-commerce[C]. In CIKM 2020.

    推荐场景

    社会推荐Social Recommendation

    DGRec: Session-based Social Recommendation via Dynamic Graph Attention Networks

  • Weiping Song, Zhiping Xiao, Yifan Wang, et al. Session-based Social Recommendation via Dynamic Graph Attention Networks[C]. In WSDM 2019.

GraphRec: Graph Neural Networks for Social Recommendation

  • Wenqi Fan, Yao Ma, Qing Li, et al. Graph Neural Networks for Social Recommendation[C]. In WWW 2019.

DANSER: Dual Graph Atention Networks for Deep Latent Representation of Multifaceted Social Efects in Recommender Systems

  • Qitian Wu, Hengrui Zhang, Xiaofeng Gao, et al. Dual Graph Atention Networks for Deep Latent Representation of Multifaceted Social Efects in Recommender Systems[C]. In WWW 2019.

DiffNet: A Neural Influence Diffusion Model for Social Recommendation

  • Le Wu, Peijie Sun, Yanjie Fu, et al. A Neural Influence Diffusion Model for Social Recommendation[C]. In SIGIR 2019.

RecoGCN: Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation

  • Fengli Xu, Jianxun Lian, Zhenyu Han, et al. Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation[C]. In CIKM 2019.

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

  • Le Wu, Junwei Li, Peijie Sun, et al. DiffNet++: A neural influence and interest diffusion network for social recommendation[J]. In TKDE 2021.

ESRF: Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

  • Junliang Yu, Hongzhi Yin, Jundong Li, et al. Enhancing Social Recommendation with Adversarial Graph Convolutional Networks[J]. In TKDE 2020.

KCGN: Knowledge-aware Coupled Graph Neural Network for Social Recommendation

  • Chao Huang, Huance Xu, Yong Xu, et al. Knowledge-aware Coupled Graph Neural Network for Social Recommendation[C]. In AAAI 2020.

MHCN: Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

  • Junliang Yu, Hongzhi Yin, Jundong Li, et al. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation[C]. In WWW 2021.

GBGCN: Group-Buying Recommendation for Social E-Commerce

  • Jun Zhang, Chen Gao, Depeng Jin, et al. Group-Buying Recommendation for Social E-Commerce[C]. In ICDE 2021.

SEPT: Socially-Aware Self-Supervised Tri-Training for Recommendation

  • Junliang Yu, Hongzhi Yin, Min Gao, et al. Socially-Aware Self-Supervised Tri-Training for Recommendation[C]. In KDD 2021.

    序列推荐Sequential Recommendation

    RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

  • Cheng Hsu, Cheng-Te Li. RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation[C]. In WWW 2021.

SURGE: Sequential Recommendation with Graph Neural Networks

  • Jianxin Chang, Chen Gao, Yu Zheng, et al. Sequential Recommendation with Graph Neural Networks[C]. In SIGIR 2021.

TGSRec: Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

  • Ziwei Fan, Zhiwei Liu, Jiawei Zhang, et al. Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer[C]. In CIKM 2021.

GES-SASRec: Graph-based Embedding Smoothing for Sequential Recommendation

  • Tianyu Zhu, Leilei Sun, Guoqing Chen. Graph-based Embedding Smoothing for Sequential Recommendation[J]. In ArXiv 2021.

会话推荐Session Recommendation

SR-GNN: Session-based Recommendation with Graph Neural Networks

  • Shu Wu, Yuyuan Tang, Yanqiao Zhu, et al. Session-based Recommendation with Graph Neural Networks[C]. In AAAI 2019.

GC-SAN: Graph Contextualized Self-Attention Network for Session-based Recommendation

  • Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, et al. Graph Contextualized Self-Attention Network for Session-based Recommendation[C]. In IJCAI 2019.

FGNN: Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

  • Ruihong Qiu, Jingjing Li, Zi Huang, et al. Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks[C]. In CIKM 2019.

MGNN-SPred: Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

  • Wen Wang, Wei Zhang, Shukai Liu, et al. Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction[C]. In WWW 2020.

LESSR: Handling Information Loss of Graph Neural Networks for Session-based Recommendation

  • Tianwen Chen, Raymond Chi-wing Wong. Handling Information Loss of Graph Neural Networks for Session-based Recommendation[C]. In KDD 2020.

TA-GNN: Target Attentive Graph Neural Networks for Session-based Recommendation

  • Feng Yu, Yanqiao Zhu, Qiang Liu, et al. TA-GNN: Target Attentive Graph Neural Networks for Session-based Recommendation[C]. In SIGIR 2020.

GCE-GNN: Global Context Enhanced Graph Neural Networks for Session-based Recommendation

  • Ziyang Wang, Wei Wei, Gao Cong, et al. Global Context Enhanced Graph Neural Networks for Session-based Recommendation[C]. In SIGIR 2020.

MKM-SR: Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation

  • Wenjing Meng, Deqing Yang, Yanghua Xiao. Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation[C]. In SIGIR 2020.

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

  • Ruihong Qiu, Hongzhi Yin, Zi Huang, et al. GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation[C]. In SIGIR 2020.

A-PGNN: Personalized graph neural networks with attention mechanism for session-aware recommendation

  • Mengqi Zhang, Shu Wu, Meng Gao, et al. Personalized graph neural networks with attention mechanism for session-aware recommendation[J]. In Entropy 2021.

DGTN: Dual-channel Graph Transition Network for Session-based Recommendation

  • Yujia Zheng, Siyi Liu, Zekun Li, et al. DGTN: Dual-channel Graph Transition Network for Session-based Recommendation[C]. In ICDMW 2020.

DHCN: Self-supervised hypergraph convolutional networks for session-based recommendation

  • Xin Xia, Hongzhi Yin, Junliang Yu, et al. Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation[C]. In AAAI 2021.

SERec: An Efficient and Effective Framework for Session-based Social Recommendation

  • Tianwen Chen, Raymond Chi-Wing Wong. An Efficient and Effective Framework for Session-based Social Recommendation[C]. In WSDM 2021.

COTREC: Self-Supervised Graph Co-Training for Session-based Recommendation

  • Xin Xia, Hongzhi Yin, Junliang Yu, et al. Self-Supervised Graph Co-Training for Session-based Recommendation[C]. In CIKM 2021.

    捆绑推荐Bundle Recommendation

    BGCN: Bundle recommendation with graph convolutional networks

  • Jianxin Chang, Chen Gao, Xiangnan He, et al. Bundle recommendation with graph convolutional networks[C]. In SIGIR 2020.

HFGN: Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

  • Xingchen Li, Xiang Wang, Xiangnan He, et al. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation[C]. In SIGIR 2020.

    跨域推荐Cross-Domain Recommendation

    PPGN: Cross-domain recommendation via preference propagation graphnet
    BiTGCF: Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks
    HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation

    多行为推荐Multi-behavior Recommendation

    MGNN-SPred: Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction

  • Wen Wang, Wei Zhang, Shukai Liu, et al. Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction[C]. In WWW 2020.

KHGT: Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

  • Lianghao Xia, Chao Huang, Yong Xu, et al. Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation[C]. In AAAI 2020.

GHCF: Graph Heterogeneous Multi-Relational Recommendation

  • Chong Chen, Weizhi Ma, Min Zhang, et al. Graph Heterogeneous Multi-Relational Recommendation[C]. In AAAI 2021.

GNMR: Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling

  • Lianghao Xia, Chao Huang, Yong Xu, et al. Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling[C]. In ICDE 2021.

DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction

  • Fengtong Xiao, Lin Li, Jingyu Zhao, et al. DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction[C]. In KDD 2021.

MB-GMN: Graph meta network for multi-behavior recommendation

  • Lianghao Xia, Yong Xu, Chao Huang, et al. Graph Meta Network for Multi-Behavior Recommendation[C]. In SIGIR 2021.

HMG-CR: Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

  • Haoran Yang, Hongxu Chen, Lin Li, et al. Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation[J]. In ArXiv 2021.

    推荐目标

    多样性推荐Diversity

    V2HT: Long-tail hashtag recommendation for micro-videos with graph convolutional network

  • Mengmeng Li, Tian Gan, Meng Liu, et al. Long-tail hashtag recommendation for micro-videos with graph convolutional network[C]. In CIKM 2019.

BGCF: A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks

  • Jianing Sun, Wei Guo, Dengcheng Zhang, et al. A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks[C]. In KDD 2020.

DGCN: Diversified Recommendation with Graph Convolutional Networks

  • Yu Zheng, Chen Gao, Liang Chen, et al. DGCN: Diversified Recommendation with Graph Convolutional Networks[C]. In WWW 2021.

    可解释性Explainability

    RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

  • Hongwei Wang, Fuzheng Zhang, Jialin Wang, et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems[C]. In CIKM 2018.

KPRN: Explainable Reasoning over Knowledge Graphs for Recommendation

  • Xiang Wang, Dingxian Wang, Canran Xu, et al. Explainable Reasoning over Knowledge Graphs for Recommendation[C]. In AAAI 2019.

RuleRec: Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

  • Weizhi Ma, Min Zhang, Yue Cao, et al. Jointly Learning Explainable Rules for Recommendation with Knowledge Graph[C]. In WWW 2019.

KGAT: Knowledge Graph Attention Network for Recommendation

  • Xiang Wang, Xiangnan He, Yixin Cao, et al. KGAT: Knowledge Graph Attention Network for Recommendation[C]. In KDD 2019.

PGPR: Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

  • Yikun Xian, Zuohui Fu, S. Muthukrishnan, et al. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation[C]. In SIGIR 2019.

ECFKG: Compositional Fairness Constraints for Graph Embeddings

  • Avishek Joey Bose, William L. Hamilton. Compositional Fairness Constraints for Graph Embeddings[C]. In ICML 2019.

TMER: Temporal meta-path guided explainable recommendation

  • Hongxu Chen, Yicong Li, Xiangguo Sun, et al. Temporal meta-path guided explainable recommendation[C]. In WSDM 2021.

    公平性Fairness

    Fairwalk: Towards fair graph embedding

  • Tahleen Rahman, Bartlomiej Surma, Michael Backes, et al. Fairwalk: Towards fair graph embedding[C]. In IJCAI 2019.

CFCGE: Compositional fairness constraints for graph embeddings

  • Avishek Joey Bose, William L. Hamilton. Compositional fairness constraints for graph embeddings[C]. In ICML 2019.

FairGNN: Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information

  • Enyan Dai, Suhang Wang. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information[C]. In WSDM 2021.

FairGo: Learning Fair Representations for Recommendation: A Graph-based Perspective

  • Le Wu, Lei Chen, Pengyang Shao, et al. Learning Fair Representations for Recommendation: A Graph-based Perspective[C]. In WWW 2021.