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{gao2022survey,
  title={A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions},
  author={Gao, Chen and Zheng, Yu and Li, Nian and Li, Yinfeng and Qin, Yingrong and Piao, Jinghua and Quan, Yuhan and Chang, Jianxin and Jin, Depeng and He, Xiangnan and Li, Yong},
  journal={ACM Transactions on Recommender Systems (TORS)},
  year={2022}
}

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).

GNN in different recommendation stages
GNN in different recommendation scenarios
GNN for different recommendation objectives

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
G³SR	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

tsinghua-fib-lab/GNN-Recommender-Systems

tsinghua-fib-lab

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