A Survey on Session-Based Recommendation Methods with Graph Neural Network
Zhang Xiongtao1,Zhu Na2(),Guo Yuhui3
1School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China 2School of National Governance, Southwest University, Chongqing 400715, China 3School of Information, Renmin University of China, Beijing 100872, China
[Objective] This paper focuses on graph neural network technology,reviewing session-based recommendation methods to provide a reference for future research. [Coverage] We took “session-based recommendation” and “graph neural network” as search terms, and 82 domestic and foreign literatures were screened from databases such as “Web of Science” and “China National Knowledge Infrastructure”. [Methods] From the perspective of framework, evaluation and trend, this paper generalises and compares session-based recommendation methods based on graph neural networks, summarises the existing evaluation resources and discusses the future research trend. [Results] Graph Neural Network is the mainstream technology for implementing session-based recommender systems. The studies on session-based recommendation methods with graph neural network mainly focus on three core problems, session graph construction, session graph learning and session interest representation. [Limitations] Session-based recommendation methods with graph neural networks are constantly emerging, and the research reviewed is only the typical research and not all studies are listed. Future research can be deepened in terms of interpretability, robustness, diversity and fairness. [Conclusions] Graph Neural Network is the mainstream technology for session-based recommender systems. Existing research has conducted preliminary exploration from various aspects and provided sufficient evaluation resources. Future research should combine the characteristics of session recommendation scenarios and develop graph neural network technology to further improve the existing research deficiencies.
Li A S, Zhu J H, Li Z Y, et al. Transition Information Enhanced Disentangled Graph Neural Networks for Session-Based Recommendation[J]. Expert Systems with Applications, 2022, 210: Article No.118336.
[2]
Wang S J, Cao L B, Wang Y, et al. A Survey on Session-Based Recommender Systems[J]. ACM Computing Surveys, 2022, 54(7): Article No.154.
[3]
Zhang X K, Lin H F, Xu B, et al. Dynamic Intent-Aware Iterative Denoising Network for Session-Based Recommendation[J]. Information Processing & Management, 2022, 59(3): Article No.102936.
[4]
Wang S J, Cao L B. Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(3): 935-946.
doi: 10.1109/TSMC.6221021
[5]
Ludewig M, Jannach D. Evaluation of Session-Based Recommendation Algorithms[J]. User Modeling and User-Adapted Interaction, 2018, 28(4-5): 331-390.
doi: 10.1007/s11257-018-9209-6
[6]
Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing Personalized Markov Chains for Next-Basket Recommendation[C]// Proceedings of the 19th International Conference on World Wide Web. ACM, 2010: 811-820.
[7]
Tavakol M, Brefeld U. Factored MDPS for Detecting Topics of User Sessions[C]// Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 2014: 33-40.
[8]
Wu Z H, Pan S R, Chen F W, et al. A Comprehensive Survey on Graph Neural Networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4-24.
doi: 10.1109/TNNLS.5962385
[9]
Hidasi B, Karatzoglou A, Baltrunas L, et al. Session-Based Recommendations with Recurrent Neural Networks[C]// Proceedings of the 4th International Conference on Learning Representations. 2016: 1-10.
[10]
Li J, Ren P J, Chen Z M, et al. Neural Attentive Session-Based Recommendation[C]// Proceedings of the 26th International Conference on Information and Knowledge Management. ACM, 2017: 1419-1428.
(Zhao Haiyan, Zhao Jiabin, Chen Qingkui, et al. Session-Based Recommendation System[J]. Journal of Chinese Computer Systems, 2019, 40(9): 1869-1875.)
[12]
Feng C Q, Shi C Y, Liu C M, et al. Context-Aware Item Attraction Model for Session-Based Recommendation[J]. Expert Systems with Applications, 2021, 176: Article No.114834.
[13]
Wu S, Tang Y Y, Zhu Y Q, et al. Session-Based Recommendation with Graph Neural Networks[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019: 346-353.
[14]
Wang Z Y, Wei W, Cong G, et al. Global Context Enhanced Graph Neural Networks for Session-Based Recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020: 169-178.
[15]
Ludewig M, Mauro N, Latifi S, et al. Performance Comparison of Neural and Non-Neural Approaches to Session-Based Recommendation[C]// Proceedings of the 13th ACM Conference on Recommender Systems. ACM, 2019: 462-466.
[16]
Wang X, He X N, Wang M, et al. Neural Graph Collaborative Filtering[C]// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2019: 165-174.
[17]
Feng L X, Cai Y Q, Wei E L, et al. Graph Neural Networks with Global Noise Filtering for Session-Based Recommendation[J]. Neurocomputing, 2022, 472: 113-123.
doi: 10.1016/j.neucom.2021.11.068
[18]
Yu J L, Yin H Z, Xia X, et al. Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022: 1294-1303.
[19]
Xu C F, Zhao P P, Liu Y C, et al. Graph Contextualized Self-Attention Network for Session-Based Recommendation[C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence. ACM, 2019: 3940-3946.
[20]
Yu F, Zhu Y Q, Liu Q, et al. TAGNN: Target Attentive Graph Neural Networks for Session-Based Recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020: 1921-1924.
[21]
Wang W, Zhang W, Liu S, et al. Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction[C]// Proceedings of the 2020 World Wide Web Conference. 2020: 3056-3062.
(Huang Zhenhua, Lin Xiaolong, Sun Shengli, et al. Feature Augmentation Based Graph Neural Recommendation Method in Session Scenarios[J]. Chinese Journal of Computers, 2022, 45(4): 766-780.)
[23]
Ma C, Ma L H, Zhang Y X, et al. Memory Augmented Graph Neural Networks for Sequential Recommendation[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020: 5045-5052.
[24]
Pan Z Q, Cai F, Chen W Y, et al. Star Graph Neural Networks for Session-Based Recommendation[C]// Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM, 2020: 1195-1204.
[25]
Xia X, Yin H Z, Yu J L, et al. Self-Supervised Hypergraph Convolutional Networks for Session-Based Recommendation[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021: 4503-4511.
[26]
Li Y F, Gao C, Luo H L, et al. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-Based Recommendation[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022: 1997-2002.
[27]
Wang J L, Ding K Z, Zhu Z W, et al. Session-Based Recommendation with Hypergraph Attention Networks[C]// Proceedings of the 21st SIAM International Conference on Data Mining. 2021: 82-90.
[28]
Xia X, Yin H Z, Yu J L, et al. Self-Supervised Graph Co-Training for Session-Based Recommendation[C]// Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, 2021: 2180-2190.
[29]
Zhang J J, Ma C H, Zhong C L, et al. MBPI: Mixed Behaviors and Preference Interaction for Session-Based Recommendation[J]. Applied Intelligence, 2021, 51(10): 7440-7452.
doi: 10.1007/s10489-021-02284-8
(Sun Wei, Shen Jianfang, Chen Pinghua. User Micro-Behavior Based on Multi-Modal Knowledge Graphs for Session-Based Recommender[J]. Journal of Chinese Computer Systems, 2023, 44(4): 706-714.)
[32]
Zhang M Q, Wu S, Gao M, et al. Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3946-3957.
doi: 10.1109/TKDE.2020.3031329
[33]
Pang Y T, Wu L F, Shen Q, et al. Heterogeneous Global Graph Neural Networks for Personalized Session-Based Recommendation[C]// Proceedings of the 15th ACM International Conference on Web Search and Data Mining. ACM, 2022: 775-783.
[34]
Chen C, Guo J, Song B. Dual Attention Transfer in Session-Based Recommendation with Multi-Dimensional Integration[C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2021: 869-878.
(Zhou Fangquan, Cheng Weiqing. Sequence Recommendation Based on Global Enhanced Graph Neural Network[J]. Computer Science, 2022, 49(9): 55-63.)
doi: 10.11896/jsjkx.210700085
[36]
Sheu H S, Chu Z X, Qi D Q, et al. Knowledge-Guided Article Embedding Refinement for Session-Based News Recommendation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12): 7921-7927.
doi: 10.1109/TNNLS.2021.3084958
[37]
Zhao P P, Luo A J, Liu Y C, et al. Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(5): 2512-2524.
doi: 10.1109/TKDE.2020.3007194
[38]
Wu S W, Sun F, Zhang W T, et al. Graph Neural Networks in Recommender Systems: A Survey[J]. ACM Computing Surveys, 55(5): Article No.97.
[39]
Qiu R H, Yin H Z, Huang Z, et al. GAG: Global Attributed Graph Neural Network for Streaming Session-Based Recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020: 669-678.
[40]
Zheng Y J, Liu S Y, Li Z K, et al. DGTN: Dual-Channel Graph Transition Network for Session-Based Recommendation[C]// Proceedings of the 2020 International Conference on Data Mining Workshop. IEEE, 2020: 236-242.
[41]
Sheu H S, Li S. Context-Aware Graph Embedding for Session-Based News Recommendation[C]// Proceedings of the 14th ACM Conference on Recommender Systems. ACM, 2020: 657-662.
[42]
Ying R, He R N, Chen K F, et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 974-983.
[43]
Liu F, Liu W W, Li X T, et al. Inter-Sequence Enhanced Framework for Personalized Sequential Recommendation[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020: 1-9.
[44]
Song W P, Xiao Z P, Wang Y F, et al. Session-Based Social Recommendation via Dynamic Graph Attention Networks[C]// Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 2019: 555-563.
[45]
Dang T K, Nguyen Q P, Nguyen V S. A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems[J]. SN Computer Science, 2020, 1(4): Article No.216.
[46]
Wang L Y, Xu X H, Ouyang K, et al. Self-Supervised Dual-Channel Attentive Network for Session-Based Social Recommendation[C]// Proceedings of the 38th International Conference on Data Engineering. IEEE, 2022: 2034-2045.
(Zhang Ruoqi, Shen Jianfang, Chen Pinghua. Session Sequence Recommendation with GNN, Bi-GRU and Attention Mechanism[J]. Data Analysis and Knowledge Discovery, 2022, 6(6): 46-54.)
[48]
Gu P, Han Y Q, Gao W, et al. Enhancing Session-Based Social Recommendation Through Item Graph Embedding and Contextual Friendship Modeling[J]. Neurocomputing, 2021, 419: 190-202.
doi: 10.1016/j.neucom.2020.08.023
[49]
Qiu R H, Li J J, Huang Z, et al. Rethinking the Item Order in Session-Based Recommendation with Graph Neural Networks[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 2019: 579-588.
[50]
Gupta P, Garg D, Malhotra P, et al. NISER: Normalized Item and Session Representations with Graph Neural Networks[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1-6.
[51]
Lai S Q, Meng E L, Zhang F, et al. An Attribute-Driven Mirror Graph Network for Session-Based Recommendation[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022: 1674-1683.
[52]
Yuan J H, Song Z H, Sun M Y, et al. Dual Sparse Attention Network for Session-Based Recommendation[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021: 4635-4643.
[53]
Chen D L, Lin Y K, Li W, et al. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020: 3438-3445.
[54]
Zhang S J, Yin H Z, Chen T, et al. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020: 689-698.
[55]
Gao C, Wang X, He X N, et al. Graph Neural Networks for Recommender System[C]// Proceedings of the 15th ACM International Conference on Web Search and Data Mining. ACM, 2022: 1623-1625.
[56]
Guo J Y, Yang Y M, Song X C, et al. Learning Multi-Granularity Consecutive User Intent Unit for Session-Based Recommendation[C]// Proceedings of the 15th ACM International Conference on Web Search and Data Mining. ACM, 2022: 343-352.
[57]
Chen D P, Zhang X M, Wang H X, et al. TEAN: Timeliness Enhanced Attention Network for Session-Based Recommendation[J]. Neurocomputing, 2020, 411: 229-238.
doi: 10.1016/j.neucom.2020.06.063
[58]
Gan M X, Zhang X T. Integrating Community Interest and Neighbor Semantic for Microblog Recommendation[J]. International Journal of Web Services Research, 2021, 18(2): 54-75.
doi: 10.4018/IJWSR
[59]
Steck H. Item Popularity and Recommendation Accuracy[C]// Proceedings of the 5th ACM Conference on Recommender Systems. ACM, 2011: 125-132.
[60]
Liu Q, Zeng Y F, Mokhosi R, et al. STAMP: Short-Term Attention/Memory Priority Model for Session-Based Recommendation[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1831-1839.
[61]
Li J, Zhang Y, Lin X, et al. TAML: Time-Aware Meta Learning for Cold-Start Problem in News Recommendation[C]// Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023: 2415-2419.
[62]
Zhang M, Wang G, Ren L, et al. METoNR: A Meta Explanation Triplet Oriented News Recommendation Model[J]. Knowledge-Based Systems, 2022, 238: 107922.
doi: 10.1016/j.knosys.2021.107922
[63]
Zhang L, Liu P, Gulla J A. A Deep Joint Network for Session-based News Recommendations with Contextual Augmentation[M]//Proceedings of the 29th on Hypertext and Social Media. 2018: 201-209.
[64]
Zhang Y, Feng F, He X, et al. Causal Intervention for Leveraging Popularity Bias in Recommendation[C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021: 11-20.
[65]
Sun Y, Yuan F, Yang M, et al. A Generic Network Compression Framework for Sequential Recommender Systems[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020: 1299-1308.
[66]
Wei Y, Wang X, Nie L, et al. MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video[C]// Proceedings of the 27th ACM International Conference on Multimedia. 2019: 1437-1445.
[67]
Chen J W, Dong H D, Wang X, et al. Bias and Debias in Recommender System: A Survey and Future Directions[OL]. arXiv Preprint, arXiv: 2010.03240.
[68]
Hui B, Zhang L Z, Zhou X, et al. Personalized Recommendation System Based on Knowledge Embedding and Historical Behavior[J]. Applied Intelligence, 2022, 52(1): 954-966.
doi: 10.1007/s10489-021-02363-w
[69]
Li L, Zhang Y F, Chen L. Personalized Prompt Learning for Explainable Recommendation[J]. ACM Transactions on Information Systems, 2023, 41(4): Article No.103.
[70]
Wang Y F, Ma W Z, Zhang M, et al. A Survey on the Fairness of Recommender Systems[J]. ACM Transactions on Information Systems, 2023, 41(3): Article No.52.
(Zhang Hai. Research on the Conceptual Framework of the Formation Mechanism of Internet Users' Information Cocoon[J]. Information Studies: Theory & Application, 2021, 44(11): 60-64.)
doi: 10.16353/j.cnki.1000-7490.2021.11.009
[72]
Ma W Z, Zhang M, Cao Y, et al. Jointly Learning Explainable Rules for Recommendation with Knowledge Graph[C]// Proceedings of the 2019 World Wide Web Conference. ACM, 2019: 1210-1221.
[73]
Huang X W, Fang Q, Qian S S, et al. Explainable Interaction-Driven User Modeling over Knowledge Graph for Sequential Recommendation[C]// Proceedings of the 27th ACM International Conference on Multimedia. ACM, 2019: 548-556.
[74]
Wang X, Wang D X, Xu C R, et al. Explainable Reasoning over Knowledge Graphs for Recommendation[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019: 5329-5336.
[75]
Yuan H, Tang J L, Hu X, et al. XGNN: Towards Model-Level Explanations of Graph Neural Networks[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2020: 430-438.
[76]
Schnake T, Eberle O, Lederer J, et al. Higher-Order Explanations of Graph Neural Networks via Relevant Walks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(11): 7581-7596.
doi: 10.1109/TPAMI.2021.3115452
[77]
Xu D K, Cheng W, Luo D S, et al. InfoGCL: Information-Aware Graph Contrastive Learning[OL]. arXiv Preprint, arXiv: 2110.15438.
[78]
Ohri K, Kumar M. Review on Self-Supervised Image Recognition Using Deep Neural Networks[J]. Knowledge-Based Systems, 2021, 224: Article No.107090.
[79]
Feng C Q, Shi C Y, Hao S F, et al. Hierarchical Social Similarity-Guided Model with Dual-Mode Attention for Session-Based Recommendation[J]. Knowledge-Based Systems, 2021, 230: Article No.107380.
[80]
Zhu T Y, Sun L L, Chen G Q. Embedding Disentanglement in Graph Convolutional Networks for Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 431-442.
[81]
Zheng Y, Wang D. A Survey of Recommender Systems with Multi-Objective Optimization[J]. Neurocomputing, 2022, 474: 141-153.
doi: 10.1016/j.neucom.2021.11.041
[82]
Zhou Y S, Zhu L, Wu C H, et al. Do the Rich Grow Richer? An Empirical Analysis of the Matthew Effect in an Online Healthcare Community[J]. Electronic Commerce Research and Applications, 2022, 52: Article No.101125.
