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An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

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GNN based Recommender Systems

An index of recommendation algorithms that are based on Graph Neural Networks. Our survey A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions is accepted by ACM Transactions on Recommender Systems. A preprint is available on arxiv: link Please cite our survey paper if this index is helpful.

@article, author=, journal=, year= > 

Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., & Li, Y. (2022). A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Transactions on Recommender Systems (TORS). 

Table of Contents

• GNN in different recommendation stages
  • Matching
  • Ranking
  • Re-ranking
  • Social Recommendation
  • Sequential Recommendation
  • Session Recommendation
  • Bundle Recommendation
  • Cross Domain Recommendation
  • Multi-behavior Recommendation
  • Diversity
  • Explainability
  • Fairness

  Recommendation Stages

  Matching

  Name	Paper	Venue	Year	Code
  GCMC	Berg, R. V. D., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263.	arxiv	2017	Python
  Pin-Sage	Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018, July). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 974-983).	KDD	2018	Python
  NGCF	Wang, X., He, X., Wang, M., Feng, F., & Chua, T. S. (2019, July). Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval (pp. 165-174).	SIGIR	2019	Python
  LightGCN	He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020, July). Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval (pp. 639-648).	SIGIR	2020	Python
  NIA-GCN	Sun, J., Zhang, Y., Guo, W., Guo, H., Tang, R., He, X., . & Coates, M. (2020, July). Neighbor interaction aware graph convolution networks for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1289-1298).	SIGIR	2020	NA
  DGCF	Wang, X., Jin, H., Zhang, A., He, X., Xu, T., & Chua, T. S. (2020, July). Disentangled graph collaborative filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1001-1010).	SIGIR	2020	Python
  IMP-GCN	Liu, F., Cheng, Z., Zhu, L., Gao, Z., & Nie, L. (2021, April). Interest-aware message-passing gcn for recommendation. In Proceedings of the Web Conference 2021 (pp. 1296-1305).	WWW	2021	Python
  SGL	Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021, July). Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 726-735).	SIGIR	2021	Python
  LT-OCF	Choi, J., Jeon, J., & Park, N. (2021). LT-OCF: Learnable-Time ODE-based Collaborative Filtering. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (pp. 251-260).	CIKM	2021	Python
  HMLET	Kong, T., Kim, T., Jeon, J., Choi, J., Lee, Y-C.,Park, N., & Kim, S-W. (2022). Linear, or Non-Linear, That is the Question! In Proceedings of the 15th ACM International Web Search and Data Mining Conference (pp. 517-525).	WSDM	2022	Python
  HS-GCN	Liu, H., Wei, Y., Yin, J., & Nie, L. (2022). HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2022	Python
  LGCN	Yu, W., Zhang, Z., & Qin, Z. (2022). Low-pass Graph Convolutional Network for Recommendation.	AAAI	2022	Python

  Ranking

  Name	Paper	Venue	Year	Code
  Fi-GNN	Li, Z., Cui, Z., Wu, S., Zhang, X., & Wang, L. (2019, November). Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 539-548).	CIKM	2019	Python
  PUP	Zheng, Y., Gao, C., He, X., Li, Y., & Jin, D. (2020, April). Price-aware recommendation with graph convolutional networks. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 133-144). IEEE.	ICDE	2020	Python
  A2-GCN	Liu, F., Cheng, Z., Zhu, L., Liu, C., & Nie, L. (2020). A2-GCN: An attribute-aware attentive GCN model for recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2020	NA
  L0-SIGN	Su, Y., Zhang, R., Erfani, S., & Xu, Z. (2021, May). Detecting Beneficial Feature Interactions for Recommender Systems. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI).	AAAI	2021	Python
  DG-ENN	Guo, W., Su, R., Tan, R., Guo, H., Zhang, Y., Liu, Z., . & He, X. (2021). Dual Graph enhanced Embedding Neural Network for CTRPrediction. arXiv preprint arXiv:2106.00314.	KDD	2021	NA
  SHCF	Li, C., Hu, L., Shi, C., Song, G., & Lu, Y. (2021). Sequence-aware Heterogeneous Graph Neural Collaborative Filtering. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) (pp. 64-72). Society for Industrial and Applied Mathematics.	SDM	2021	Python
  GCM	Wu, J., He, X., Wang, X., Wang, Q., Chen, W., Lian, J., & Xie, X. (2020). Graph Convolution Machine for Context-aware Recommender System. arXiv preprint arXiv:2001.11402.	Frontiers of Computer Science	2021	Python
  TGIN	Jiang, W., Jiao, Y., Wang, Q., Liang, C., Guo, L., Zhang, Y., . & Zhu, Y. (2022, February). Triangle Graph Interest Network for Click-through Rate Prediction. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 401-409).	WSDM	2022	Python

  Re-ranking

  Name	Paper	Venue	Year	Code
  IRGPR	Liu, W., Liu, Q., Tang, R., Chen, J., He, X., & Heng, P. A. (2020, October). Personalized Re-ranking with Item Relationships for E-commerce. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 925-934).	CIKM	2020	NA

  Recommendation Scenarios

  Social Recommendation

  Name	Paper	Venue	Year	Code
  DiffNet	Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., & Wang, M. (2019, July). A neural influence diffusion model for social recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 235-244).	SIGIR	2019	Python
  GraphRec	Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., & Yin, D. (2019, May). Graph neural networks for social recommendation. In The World Wide Web Conference (pp. 417-426).	WWW	2019	Python
  DANSER	Wu, Q., Zhang, H., Gao, X., He, P., Weng, P., Gao, H., & Chen, G. (2019, May). Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In The World Wide Web Conference (pp. 2091-2102).	WWW	2019	Python
  DGRec	Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., & Tang, J. (2019, January). Session-based social recommendation via dynamic graph attention networks. In Proceedings of the Twelfth ACM international conference on web search and data mining (pp. 555-563).	WSDM	2019	Python
  HGP	Kim, K. M., Kwak, D., Kwak, H., Park, Y. J., Sim, S., Cho, J. H., . & Ha, J. W. (2019). Tripartite heterogeneous graph propagation for large-scale social recommendation. arXiv preprint arXiv:1908.02569.	RecSys	2019	NA
  DiffNet++	Wu, L., Li, J., Sun, P., Hong, R., Ge, Y., & Wang, M. (2020). Diffnet++: A neural influence and interest diffusion network for social recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2020	Python
  MHCN	Yu, J., Yin, H., Li, J., Wang, Q., Hung, N. Q. V., & Zhang, X. (2021, April). Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In Proceedings of the Web Conference 2021 (pp. 413-424).	WWW	2021	Python
  SEPT	Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., & Hung, N. Q. V. (2021). Socially-Aware Self-Supervised Tri-Training for Recommendation. arXiv preprint arXiv:2106.03569.	KDD	2021	Python
  GBGCN	Zhang, J., Gao, C., Jin, D., & Li, Y. (2021, April). Group-Buying Recommendation for Social E-Commerce. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 1536-1547). IEEE.	ICDE	2021	Python
  KCGN	Huang, C., Xu, H., Xu, Y., Dai, P., Xia, L., Lu, M., . & Ye, Y. (2021, January). Knowledge-aware coupled graph neural network for social recommendation. In AAAI Conference on Artificial Intelligence (AAAI).	AAAI	2021	Python
  DiffNetLG	Song, C., Wang, B., Jiang, Q., Zhang, Y., He, R., & Hou, Y. (2021, July). Social Recommendation with Implicit Social Influence. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1788-1792).	SIGIR	2021	NA
  RecoGCN	Xu, F., Lian, J., Han, Z., Li, Y., Xu, Y., & Xie, X. (2019, November). Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 529-538).	CIKM	2019	Python
  GAT-NSR	Mu, N., Zha, D., He, Y., & Tang, Z. (2019, November). Graph attention networks for neural social recommendation. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1320-1327). IEEE.	ICTAI	2019	NA
  SR-HGNN	Xu, H., Huang, C., Xu, Y., Xia, L., Xing, H., & Yin, D. (2020, November). Global context enhanced social recommendation with hierarchical graph neural networks. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 701-710). IEEE.	ICDM	2020	Python
  TGRec	Bai, T., Zhang, Y., Wu, B., & Nie, J. Y. (2020, December). Temporal Graph Neural Networks for Social Recommendation. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 898-903). IEEE.	ICBD	2020	NA
  ESRF	Yu, J., Yin, H., Li, J., Gao, M., Huang, Z., & Cui, L. (2020). Enhance social recommendation with adversarial graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2020	Python
  HOSR	Liu, Y., Liang, C., He, X., Peng, J., Zheng, Z., & Tang, J. (2020). Modelling high-order social relations for item recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2020	NA
  GNN-SoR	Guo, Z., & Wang, H. (2020). A deep graph neural network-based mechanism for social recommendations. IEEE Transactions on Industrial Informatics, 17(4), 2776-2783.	TII	2020	NA
  ASR	Luo, D., Bian, Y., Zhang, X., & Huan, J. (2020). Attentive Social Recommendation: Towards User And Item Diversities. arXiv preprint arXiv:2011.04797.	arxiv	2020	Python

  Sequential Recommendation

  Name	Paper	Venue	Year	Code
  ISSR	Liu, F., Liu, W., Li, X., & Ye, Y. (2020). Inter-sequence Enhanced Framework for Personalized Sequential Recommendation. arXiv preprint arXiv:2004.12118.	AAAI	2020	NA
  MA-GNN	Ma, C., Ma, L., Zhang, Y., Sun, J., Liu, X., & Coates, M. (2020, April). Memory augmented graph neural networks for sequential recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 5045-5052).	AAAI	2020	NA
  STP-UDGAT	Lim, N., Hooi, B., Ng, S. K., Wang, X., Goh, Y. L., Weng, R., & Varadarajan, J. (2020, October). STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 845-854).	CIKM	2020	NA
  GPR	Chang, B., Jang, G., Kim, S., & Kang, J. (2020, October). Learning graph-based geographical latent representation for point-of-interest recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 135-144).	CIKM	2020	NA
  GES-SASRec	Zhu, T., Sun, L., & Chen, G. (2021). Graph-based Embedding Smoothing for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2021	Python
  RetaGNN	Hsu, C., & Li, C. T. (2021, April). RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation. In Proceedings of the Web Conference 2021 (pp. 2968-2979).	WWW	2021	Python
  TGSRec	Fan, Z., Liu, Z., Zhang, J., Xiong, Y., Zheng, L., & Yu, P. S. (2021). Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer. arXiv preprint arXiv:2108.06625.	CIKM	2021	Python
  SGRec	Li, Y., Chen, T., Yin, H., & Huang, Z. (2021). Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation. arXiv preprint arXiv:2106.15814.	IJCAI	2021	NA
  SURGE	Chang, J., Gao, C., Zheng, Y., Hui, Y., Niu, Y., Song, Y., . & Li, Y. (2021, July). Sequential Recommendation with Graph Neural Networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 378-387).	SIGIR	2021	Python
  GME	Xie, M., Yin, H., Xu, F., Wang, H., & Zhou, X. (2016, November). Graph-based metric embedding for next poi recommendation. In International Conference on Web Information Systems Engineering (pp. 207-222). Springer, Cham.	WISE	2016	NA
  Wang et al.	Wang, B., & Cai, W. (2020). Knowledge-enhanced graph neural networks for sequential recommendation. Information, 11(8), 388.	Information	2020	NA
  DGSR	Zhang, M., Wu, S., Yu, X., & Wang, L. (2021). Dynamic Graph Neural Networks for Sequential Recommendation. arXiv preprint arXiv:2104.07368.	arxiv	2021	NA

  Session Recommendation

  Name	Paper	Venue	Year	Code
  SR-GNN	Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019, July). Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 346-353).	AAAI	2019	Python
  GC-SAN	Xu, C., Zhao, P., Liu, Y., Sheng, V. S., Xu, J., Zhuang, F., . & Zhou, X. (2019, August). Graph Contextualized Self-Attention Network for Session-based Recommendation. In IJCAI (Vol. 19, pp. 3940-3946).	IJCAI	2019	Python
  TA-GNN	Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., & Tan, T. (2020, July). TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1921-1924).	SIGIR	2020	Python
  MGNN-SPred	Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., & Zha, H. (2020, April). Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In Proceedings of The Web Conference 2020 (pp. 3056-3062).	WWW	2020	Python
  LESSR	Chen, T., & Wong, R. C. W. (2020, August). Handling information loss of graph neural networks for session-based recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1172-1180).	KDD	2020	Python
  MKM-SR	Meng, W., Yang, D., & Xiao, Y. (2020, July). Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1091-1100).	SIGIR	2020	Python
  GAG	Qiu, R., Yin, H., Huang, Z., & Chen, T. (2020, July). Gag: Global attributed graph neural network for streaming session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 669-678).	SIGIR	2020	Python
  GCE-GNN	Wang, Z., Wei, W., Cong, G., Li, X. L., Mao, X. L., & Qiu, M. (2020, July). Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 169-178).	SIGIR	2020	Python
  SGNN-HN	Pan, Z., Cai, F., Chen, W., Chen, H., & de Rijke, M. (2020, October). Star graph neural networks for session-based recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 1195-1204).	CIKM	2020	NA
  DHCN	Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., & Zhang, X. (2020). Self-supervised hypergraph convolutional networks for session-based recommendation. arXiv preprint arXiv:2012.06852.	AAAI	2021	Python
  SHARE	Wang, J., Ding, K., Zhu, Z., & Caverlee, J. (2021). Session-based Recommendation with Hypergraph Attention Networks. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) (pp. 82-90). Society for Industrial and Applied Mathematics.	SDM	2021	NA
  SERec	Chen, T., & Wong, R. C. W. (2021, March). An Efficient and Effective Framework for Session-based Social Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 400-408).	WSDM	2021	Python
  COTREC	Xia, X., Yin, H., Yu, J., Shao, Y., & Cui, L. (2021). Self-Supervised Graph Co-Training for Session-based Recommendation. arXiv preprint arXiv:2108.10560.	CIKM	2021	Python
  DAT-MDI	Chen, C., Guo, J., & Song, B. (2021, July). Dual Attention Transfer in Session-based Recommendation with Multi-dimensional Integration. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 869-878).	SIGIR	2021	NA
  TASRec	Zhou, H., Tan, Q., Huang, X., Zhou, K., & Wang, X. (2021). Temporal Augmented Graph Neural Networks for Session-Based Recommendations.	SIGIR	2021	NA
  G 3 SR	Deng, Z. H., Wang, C. D., Huang, L., Lai, J. H., & Philip, S. Y. (2022). G^ 3SR: Global Graph Guided Session-Based Recommendation. IEEE Transactions on Neural Networks and Learning Systems.	TNNLS	2022	NA
  HG-GNN	Pang, Y., Wu, L., Shen, Q., Zhang, Y., Wei, Z., Xu, F., . & Pei, J. (2022, February). Heterogeneous global graph neural networks for personalized session-based recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (pp. 775-783).	WSDM	2022	Python
  CGL	Pan, Z., Cai, F., Chen, W., Chen, C., & Chen, H. (2022). Collaborative Graph Learning for Session-based Recommendation. ACM Transactions on Information Systems (TOIS), 40(4), 1-26.	TOIS	2022	NA
  CAGE	Sheu, H. S., & Li, S. (2020, September). Context-aware graph embedding for session-based news recommendation. In Fourteenth ACM conference on recommender systems (pp. 657-662).	RecSys	2020	NA
  A-PGNN	Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., & Wang, L. (2020). Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2020	Python
  DGTN	Zheng, Y., Liu, S., Li, Z., & Wu, S. (2020, November). DGTN: Dual-channel Graph Transition Network for Session-based Recommendation. In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 236-242). IEEE.	ICDMW	2020	Python
  FGNN	Qiu, R., Li, J., Huang, Z., & Yin, H. (2019, November). Rethinking the item order in session-based recommendation with graph neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 579-588).	CIKM	2019	Python

  Bundle Recommendation

  Name	Paper	Venue	Year	Code
  BGCN	Chang, J., Gao, C., He, X., Jin, D., & Li, Y. (2020, July). Bundle recommendation with graph convolutional networks. In Proceedings of the 43rd international ACM SIGIR conference on Research and development in Information Retrieval (pp. 1673-1676).	SIGIR	2020	Python
  HFGN	Li, X., Wang, X., He, X., Chen, L., Xiao, J., & Chua, T. S. (2020, July). Hierarchical fashion graph network for personalized outfit recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 159-168).	SIGIR	2020	Python
  BundleNet	Deng, Q., Wang, K., Zhao, M., Zou, Z., Wu, R., Tao, J., . & Chen, L. (2020, October). Personalized Bundle Recommendation in Online Games. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 2381-2388).	CIKM	2020	NA
  DPR	Zheng, Z., Wang, C., Xu, T., Shen, D., Qin, P., Huai, B., . & Chen, E. (2021, April). Drug Package Recommendation via Interaction-aware Graph Induction. In Proceedings of the Web Conference 2021 (pp. 1284-1295).	WWW	2021	NA
  DPG	Zheng, Z., Wang, C., Xu, T., Shen, D., Qin, P., Zhao, X., . & Chen, E. (2022). Interaction-aware Drug Package Recommendation via Policy Gradient. ACM Transactions on Information Systems (TOIS).	TOIS	2022	NA
  MIDGN	Zhao, S., Wei, W., Zou, D., & Mao, X. (2022). Multi-view intent disentangle graph networks for bundle recommendation. arXiv preprint arXiv:2202.11425.	AAAI	2022	Python

  Cross Domain Recommendation

  Name	Paper	Venue	Year	Code
  PPGN	Zhao, C., Li, C., & Fu, C. (2019, November). Cross-domain recommendation via preference propagation graphnet. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2165-2168).	CIKM	2019	Python
  BiTGCF	Liu, M., Li, J., Li, G., & Pan, P. (2020, October). Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 885-894).	CIKM	2020	Python
  DAN	Wang, B., Zhang, C., Zhang, H., Lyu, X., & Tang, Z. (2020, October). Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 2249-2252).	CIKM	2020	NA
  HeroGRAPH	Cui, Q., Wei, T., Zhang, Y., & Zhang, Q. (2020). HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation. In ORSUM@ RecSys.	RecSys	2020	Python
  DAGCN	Guo, L., Tang, L., Chen, T., Zhu, L., Nguyen, Q. V. H., & Yin, H. (2021). DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation. arXiv preprint arXiv:2105.03300.	IJCAI	2021	NA

  Recommendation Objectives

  Multi-behavior Recommendation

  Name	Paper	Venue	Year	Code
  MBGCN	Jin, B., Gao, C., He, X., Jin, D., & Li, Y. (2020, July). Multi-behavior recommendation with graph convolutional networks. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 659-668).	SIGIR	2020	Python
  MGNN-SPred	Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., & Zha, H. (2020, April). Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In Proceedings of The Web Conference 2020 (pp. 3056-3062).	WWW	2020	Python
  MGNN	Zhang, W., Mao, J., Cao, Y., & Xu, C. (2020, October). Multiplex Graph Neural Networks for Multi-behavior Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 2313-2316).	CIKM	2020	NA
  LP-MRGNN	Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., & Zha, H. (2021). Incorporating Link Prediction into Multi-Relational Item Graph Modeling for Session-based Recommendation. IEEE Transactions on Knowledge and Data Engineering.	TKDE	2021	NA
  GNMR	Xia, L., Huang, C., Xu, Y., Dai, P., Lu, M., & Bo, L. (2021, April). Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 1931-1936). IEEE.	ICDE	2021	Python
  MB-GMN	Xia, L., Xu, Y., Huang, C., Dai, P., & Bo, L. (2021, July). Graph meta network for multi-behavior recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 757-766).	SIGIR	2021	Python
  KHGT	Xia, L., Huang, C., Xu, Y., Dai, P., Zhang, X., Yang, H., . & Bo, L. (2021, May). Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4486-4493).	AAAI	2021	Python
  GHCF	Chen, C., Ma, W., Zhang, M., Wang, Z., He, X., Wang, C., . & Ma, S. (2021, May). Graph Heterogeneous Multi-Relational Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 3958-3966).	AAAI	2021	Python
  DMBGN	Xiao, F., Li, L., Xu, W., Zhao, J., Yang, X., Lang, J., & Wang, H. (2021). DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction. arXiv preprint arXiv:2106.03356.	KDD	2021	Python
  HMG-CR	Yang, H., Chen, H., Li, L., Yu, P. S., & Xu, G. (2021). Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation. arXiv preprint arXiv:2109.02859.	ICDM	2021	Python
  GNNH	Yu, B., Zhang, R., Chen, W., & Fang, J. (2021). Graph neural network based model for multi-behavior session-based recommendation. GeoInformatica, 1-19.	GeoInformatica	2021	NA

  Diversity

  Name	Paper	Venue	Year	Code
  V2HT	Li, M., Gan, T., Liu, M., Cheng, Z., Yin, J., & Nie, L. (2019, November). Long-tail hashtag recommendation for micro-videos with graph convolutional network. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 509-518).	CIKM	2019	NA
  BGCF	Sun, J., Guo, W., Zhang, D., Zhang, Y., Regol, F., Hu, Y., . & Coates, M. (2020, August). A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2030-2039).	KDD	2020	Python
  DGCN	Zheng, Y., Gao, C., Chen, L., Jin, D., & Li, Y. (2021, April). DGCN: Diversified Recommendation with Graph Convolutional Networks. In Proceedings of the Web Conference 2021 (pp. 401-412).	WWW	2021	Python
  FH-HAT	Xie, R., Liu, Q., Liu, S., Zhang, Z., Cui, P., Zhang, B., & Lin, L. (2021). Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network. arXiv preprint arXiv:2102.03787.	TBD	2021	NA
  Isufi et al.	Isufi, E., Pocchiari, M., & Hanjalic, A. (2021). Accuracy-diversity trade-off in recommender systems via graph convolutions. Information Processing & Management, 58(2), 102459.	IPM	2021	Python

  Explainability

  Name	Paper	Venue	Year	Code
  RippleNet	Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2018, October). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 417-426).	CIKM	2018	Python
  EIUM	Huang, X., Fang, Q., Qian, S., Sang, J., Li, Y., & Xu, C. (2019, October). Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 548-556).	MM	2019	NA
  KPRN	Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T. S. (2019, July). Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 5329-5336).	AAAI	2019	Python
  RuleRec	Ma, W., Zhang, M., Cao, Y., Jin, W., Wang, C., Liu, Y., . & Ren, X. (2019, May). Jointly learning explainable rules for recommendation with knowledge graph. In The World Wide Web Conference (pp. 1210-1221).	WWW	2019	Python
  PGPR	Xian, Y., Fu, Z., Muthukrishnan, S., De Melo, G., & Zhang, Y. (2019, July). Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 285-294).	SIGIR	2019	Python
  KGAT	Wang, X., He, X., Cao, Y., Liu, M., & Chua, T. S. (2019, July). Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 950-958).	KDD	2019	Python
  TMER	Chen, H., Li, Y., Sun, X., Xu, G., & Yin, H. (2021, March). Temporal meta-path guided explainable recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 1056-1064).	WSDM	2021	Python
  ECFKG	Bose, A., & Hamilton, W. (2019, May). Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning (pp. 715-724). PMLR.	ICML	2019	Python
  HAGERec	Yang, Z., & Dong, S. (2020). HAGERec: hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation. Knowledge-Based Systems, 204, 106194.	KBS	2020	NA

  Fairness

  Name	Paper	Venue	Year	Code
  FairGo	Wu, L., Chen, L., Shao, P., Hong, R., Wang, X., & Wang, M. (2021, April). Learning Fair Representations for Recommendation: A Graph-based Perspective. In Proceedings of the Web Conference 2021 (pp. 2198-2208).	WWW	2021	Python
  FairGNN	Dai, E., & Wang, S. (2021, March). Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 680-688).	WSDM	2021	Python
  Fairwalk	Rahman, T., Surma, B., Backes, M., & Zhang, Y. (2019). Fairwalk: Towards fair graph embedding.	IJCAI	2019	Python
  CFCGE	Bose, A., & Hamilton, W. (2019, May). Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning (pp. 715-724). PMLR.	ICML	2019	Python

  About

  An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)