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

Related tags

Deep Learninginformation-retrievalrecommendation-systemrecommender-systemrecommendationrecommendation-algorithmsgraph-convolutional-networksgcngraph-neural-networksgraph-representation-learninggnn
Overview

GNN based Recommender Systems

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

Our survey Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions is available on arxiv: link

Table of Contents

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
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
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
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

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
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

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
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
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
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
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
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
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
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
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
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
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
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
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
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

Sequential Recommendation

Name Paper Venue Year Code
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
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
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
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
Wang et al. Wang, B., & Cai, W. (2020). Knowledge-enhanced graph neural networks for sequential recommendation. Information, 11(8), 388. Information 2020 NA
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
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
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 NA
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
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
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
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
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
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
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
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
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
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
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
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
TASRec Zhou, H., Tan, Q., Huang, X., Zhou, K., & Wang, X. (2021). Temporal Augmented Graph Neural Networks for Session-Based Recommendations. SIGIR 2021 NA
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
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
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

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

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

Multi-behavior Recommendation

Name Paper Venue Year Code
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
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 NA
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
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
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
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
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
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
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
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

Recommendation Objectives

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
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
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
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
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
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
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
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

Fairness

Name Paper Venue Year Code
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
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
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
Owner
FIB LAB, Tsinghua University
FIB LAB, Tsinghua University
GitHub Repository
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks This is a Pytorch-Lightning implementation of the paper "Self-s

Photogrammetry & Robotics Bonn 111 Dec 06, 2022
A simple consistency training framework for semi-supervised image semantic segmentation

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation PseudoSeg is a simple consistency training framework for semi-supervised image semantic s

Google Interns 143 Dec 13, 2022
Project for music generation system based on object tracking and CGAN

Project for music generation system based on object tracking and CGAN The project was inspired by MIDINet: A Convolutional Generative Adversarial Netw

1 Nov 21, 2021
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation

Mining Latent Classes for Few-shot Segmentation Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao. This codebase contains baseline of our paper Mini

Lihe Yang 66 Nov 29, 2022
TensorFlow 101: Introduction to Deep Learning for Python Within TensorFlow

TensorFlow 101: Introduction to Deep Learning I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmar

Sefik Ilkin Serengil 896 Jan 04, 2023
Code for "Typilus: Neural Type Hints" PLDI 2020

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

47 Nov 08, 2022
CROSS-LINGUAL ABILITY OF MULTILINGUAL BERT: AN EMPIRICAL STUDY

M-BERT-Study CROSS-LINGUAL ABILITY OF MULTILINGUAL BERT: AN EMPIRICAL STUDY Motivation Multilingual BERT (M-BERT) has shown surprising cross lingual a

CogComp 1 Feb 28, 2022
An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Raspberry Pi Air Quality Monitor A simple air quality monitoring service for the Raspberry Pi. Installation Clone the repository and run the following

rydercalmdown 24 Dec 09, 2022
Adversarial Attacks are Reversible via Natural Supervision

Adversarial Attacks are Reversible via Natural Supervision ICCV2021 Citation @InProceedings{Mao_2021_ICCV, author = {Mao, Chengzhi and Chiquier

Computer Vision Lab at Columbia University 20 May 22, 2022
SOTA model in CIFAR10

A PyTorch Implementation of CIFAR Tricks 调研了CIFAR10数据集上各种trick，数据增强，正则化方法，并进行了实现。目前项目告一段落，如果有更好的想法，或者希望一起维护这个项目可以提issue或者在我的主页找到我的联系方式。 0. Requirement

PJDong 58 Dec 21, 2022
A python library for highly configurable transformers - easing model architecture search and experimentation.

A python library for highly configurable transformers - easing model architecture search and experimentation.

Anthony Fuller 51 Nov 20, 2022
PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

Simple and Deep Graph Convolutional Networks This repository contains a PyTorch implementation of "Simple and Deep Graph Convolutional Networks".(http

chenm 253 Dec 08, 2022
ParaGen is a PyTorch deep learning framework for parallel sequence generation

ParaGen is a PyTorch deep learning framework for parallel sequence generation. Apart from sequence generation, ParaGen also enhances various NLP tasks, including sequence-level classification, extrac

Bytedance Inc. 169 Dec 22, 2022
Pose Detection and Machine Learning for real-time body posture analysis during exercise to provide audiovisual feedback on improvement of form.

Posture: Pose Tracking and Machine Learning for prescribing corrective suggestions to improve posture and form while exercising. This repository conta

Pratham Mehta 10 Nov 11, 2022
A Novel Plug-in Module for Fine-grained Visual Classification

Pytorch implementation for A Novel Plug-in Module for Fine-Grained Visual Classification. fine-grained visual classification task.

ChouPoYung 109 Dec 20, 2022
NAVER BoostCamp Final Project

CV 14조 final project Super Resolution and Deblur module Inference code & Pretrained weight Repo SwinIR Deblur 실행 방법 streamlit run WebServer/Server_SRD

JiSeong Kim 5 Sep 06, 2022
Subpopulation detection in high-dimensional single-cell data

PhenoGraph for Python3 PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") repr

Dana Pe'er Lab 42 Sep 05, 2022
A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

RE2 This is a pytorch implementation of the ACL 2019 paper "Simple and Effective Text Matching with Richer Alignment Features". The original Tensorflo

287 Dec 21, 2022
A curated list of awesome Active Learning

Awesome Active Learning 🤩 A curated list of awesome Active Learning ! 🤩 Background (image source: Settles, Burr) What is Active Learning? Active lea

BAI Fan 431 Jan 03, 2023