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MVC-HGAT: multi-view contrastive hypergraph attention network for session-based recommendation

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Abstract

Session-based recommendation (SBR) mainly analyzes user’s interaction sequences and recommends a list of items for the next potential interaction. The existing models of SBR mostly obtain different item features through dual-channel graph neural networks, but there are often many different views of high-order relationships hidden in the real session sequence. Especially, the sparsity of the interaction sequence affects the performance of the SBR model. To make the recommendation results more comprehensive and accurate, we propose a multi-view contrastive hypergraph attention network (MVC-HGAT) for session-based recommendation, which models the session sequence as multi-view hypergraphs from three different views: the context relationship of the interaction sequence, the click unit and the hidden similarity attribute of items. The multi-view feature information of items is captured by hypergraph attention network (HGAT) and fused by sum-pooling. Additionally, multi-view contrastive learning is employed to alleviate data sparsity in the hypergraph. To prevent fitting, label smoothing is introduced in the loss function. Extensive experiment results on selected real datasets, including Diginetica, Yoochoose and Retailrocket, demonstrate that our proposed MVC-HGAT has improved recommendation performance to some extent, and is better than the baselines for two metrics Prec@20 and MRR@20.

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Availability of Data and Materials

The authors declare that the data supporting the findings of this study are available within the article.

Notes

  1. http://cikm2016.cs.iupui.edu/cikm-cup/

  2. http://2015.recsyschallenge.com/challege.html

  3. https://www.kaggle.com/retailrocket/ecommerce-dataset

  4. https://github.com/hidasib/GRU4Rec/

  5. https://github.com/lijingsdu/sessionRec_NARM

  6. https://github.com/uestcnlp/STAMP

  7. https://github.com/CRIPAC-DIG/SR-GNN

  8. https://github.com/johnny12150/GCE-GNN

  9. https://github.com/SpaceLearner/SessionRec-pytorch

  10. https://github.com/Peiyance/Atten-Mixer-torch

  11. https://github.com/jin530/MiaSRec

  12. https://github.com/yf-li15/HIDE

  13. https://github.com/xiaxin1998/DHCN

  14. https://github.com/xiaxin1998/COTREC

  15. https://github.com/akaxlh/HCCF

  16. https://github.com/Zhang-xiaokun/DIMO

References

  1. Cai Y, Li J (2022) Rethinking transition relationship between co-occurring items in graph neural networks for session-based recommendation. Appl Soft Comput 126:109231

    Article  MATH  Google Scholar 

  2. Chen J, Wang C, Zhou S, Shi Q, Chen J, Feng Y, Chen C (2020) Fast adaptively weighted matrix factorization for recommendation with implicit feedback. In: The Thirty-Fourth AAAI conference on artificial intelligence, AAAI 2020, the thirty-second innovative applications of artificial intelligence conference. IAAI 2020, The Tenth AAAI symposium on educational advances in artificial intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. AAAI Press, pp 3470–3477

  3. Chen Y, Qian W, Liu D, Su Y, Zhou Y, Han J, Li R (2022) Contrastive learning for session-based recommendation. In: Pimenidis E, Angelov PP, Jayne C, Papaleonidas A, Aydin M (eds), Artificial Neural Networks and Machine Learning - ICANN 2022 - 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6-9, 2022, Proceedings, Part IV, Springer volume 13532 of Lecture Notes in Computer Science, pp 358–369

  4. Choi M, Kim H, Cho H, Lee J (2024) Multi-intent-aware session-based recommendation. In: Yang GH, Wang H, Han S, Hauff C, Zuccon G, Zhang Y (eds) Proceedings of the 47th International ACM SIGIR conference on research and development in information retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024. ACM, pp 2532–2536

  5. Faggioli G, Polato M, Aiolli F (2020) Recency aware collaborative filtering for next basket recommendation. In: Kuflik T, Torre I, Burke R, Gena C (eds), Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, July 12-18, 2020. ACM, pp 80–87

  6. Guo J, Yang Y, Song X, Zhang Y, Wang Y, Bai J, Zhang Y (2022) Learning multi-granularity consecutive user intent unit for session-based recommendation. In: Candan KS, Liu H, Akoglu L, Dong XL, Tang J (eds) WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21–25, 2022. ACM, pp 343–352

  7. Guo Q, Sun Z, Zhang J, Theng Y (2020) An attentional recurrent neural network for personalized next location recommendation. In: The Thirty-Fourth AAAI conference on artificial intelligence, AAAI 2020, The thirty-second innovative applications of artificial intelligence conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. AAAI Press, pp 83–90

  8. Han J, Tao Q, Tang Y, Xia Y (2022) DH-HGCN: dual homogeneity hypergraph convolutional network for multiple social recommendations. In: Amigó E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, pp 2190–2194

  9. Han Q, Zhang C, Chen R, Lai R, Song H, Li L (2022) Multi-faceted global item relation learning for session-based recommendation. In: Amigó E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, pp 1705–1715

  10. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: Bengio Y, LeCun Y (eds) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings

  11. Hou Y, Hu B, Zhang Z, Zhao WX (2022) CORE: simple and effective session-based recommendation within consistent representation space. In: Amigó E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, pp 1796–1801

  12. Huang C, Chen J, Xia L, Xu Y, Dai P, Chen Y, Bo L, Zhao J, Huang JX (2021) Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, pp 4123–4130

  13. Li A, Cheng Z, Liu F, Gao Z, Guan W, Peng Y (2023) Disentangled graph neural networks for session-based recommendation. IEEE Trans Knowl Data Eng 35:7870–7882

    MATH  Google Scholar 

  14. Li J, Ren P, Chen Z, Ren Z, Lian T Ma J (2017) Neural attentive session-based recommendation. In: Lim E, Winslett M, Sanderson M, Fu AW, Sun J, Culpepper JS, Lo E, Ho JC, Donato D, Agrawal R, Zheng Y, Castillo C, Sun A, Tseng VS, Li C (eds) Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM 2017, Singapore, November 06 - 10, 2017. ACM, pp 1419–1428

  15. Li Y, Gao C, Luo H, Jin D, Li Y (2022) Enhancing hypergraph neural networks with intent disentanglement for session-based recommendation. In: Amigó E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, pp 1997–2002

  16. Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) STAMP: short-term attention/memory priority model for session-based recommendation. In: Guo Y, Farooq F (eds) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018. ACM, pp 1831–1839

  17. Pan Z, Cai F, Chen W, Chen H, de Rijke M (2020) Star graph neural networks for session-based recommendation. In: d’Aquin M, Dietze S, Hauff C, Curry E, Cudré-Mauroux P (eds) CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19–23, 2020. ACM, pp 1195–1204

  18. Peng D, Zhang S (2022) GC-HGNN: A global-context supported hypergraph neural network for enhancing session-based recommendation. Electron Commer Res Appl 52:101129

    Article  Google Scholar 

  19. Verma M, Patnaik PK (2024) An automatic college library book recommendation system using optimized hidden markov based weighted fuzzy ranking model. Eng Appl Artif Intell 130:107664

    Article  Google Scholar 

  20. Wang H, Yan S, Wu C, Han L, Zhou L (2023) Cross-view temporal graph contrastive learning for session-based recommendation. Knowl Based Syst 264:110304

    Article  MATH  Google Scholar 

  21. Wang J, Ding K, Zhu Z, Caverlee J (2021) Session-based recommendation with hypergraph attention networks. In: Demeniconi C, Davidson I (eds) Proceedings of the 2021 SIAM international conference on data mining, SDM 2021, Virtual Event, April 29 - May 1, 2021. SIAM, pp 82–90

  22. Wang J, Lee L-K, Wu N-I (2022) Dual-channel convolutional recurrent networks for session-based recommendation. In: Agrawal DP, Nedjah N, Gupta BB, Martinez Perez G (eds) Cyber Security, Privacy and Networking. Springer Nature Singapore, Singapore, pp 287–296

    Chapter  MATH  Google Scholar 

  23. Wang S, Cao L, Wang Y, Sheng QZ, Orgun MA, Lian D (2022) A survey on session-based recommender systems. ACM Comput Surv 54:154:1–154:38

  24. Wang Z, Wei W, Cong G, Li X, Mao X, Qiu M (2020) Global context enhanced graph neural networks for session-based recommendation. In: Huang JX, Chang Y, Cheng X, Kamps J, Murdock V, Wen J, Liu Y (eds) Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020. ACM, pp 169–178

  25. Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, pp 346–353

  26. Xia L, Huang C, Xu Y, Zhao J, Yin D, Huang JX (2022) Hypergraph contrastive collaborative filtering. In: Amigó E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, pp 70–79

  27. Xia X, Yin H, Yu J, Shao Y, Cui L (2021) Self-supervised graph co-training for session-based recommendation. In: Demartini G, Zuccon G, Culpepper JS, Huang Z, Tong H (eds) CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1–5, 2021. ACM, pp 2180–2190

  28. Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. In: Thirty-Fifth AAAI conference on artificial intelligence, AAAI 2021, thirty-third conference on innovative applications of artificial intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, pp 4503–4511

  29. Yang T, Yang C, Zhang L, Shi C, Hu M, Liu H, Li T, Wang D (2022) Co-clustering interactions via attentive hypergraph neural network. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

  30. Yu F, Zhu Y, Liu Q, Wu S, Wang L, Tan T (2020) TAGNN: target attentive graph neural networks for session-based recommendation. In: Huang JX, Chang Y, Cheng X, Kamps J, Murdock V, Wen J, Liu Y (eds) Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020. ACM, pp 1921–1924

  31. Yu J, Yin H, Li J, Wang Q, Hung NQV, Zhang X (2021) Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Leskovec J, Grobelnik M, Najork M, Tang J, Zia L (eds) WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021. ACM / IW3C2, pp 413–424

  32. Yuan J, Ji W, Zhang D, Pan J, Wang X (2022) Micro-behavior encoding for session-based recommendation. In: 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022. IEEE, pp 2886–2899

  33. Zhang P, Guo J, Li C, Xie Y, Kim J, Zhang Y, Xie X, Wang H, Kim S (2023) Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network. In: Chua T, Lauw HW, Si L, Terzi E, Tsaparas P (eds) Proceedings of the Sixteenth ACM International conference on web search and data mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023. ACM, pp 168–176

  34. Zhang X, Ma H, Yang F, Li Z, Chang L (2023) KGCL: A knowledge-enhanced graph contrastive learning framework for session-based recommendation. Eng Appl Artif Intell 124:106512

    Article  Google Scholar 

  35. Zhang X, Xu B, Ren Z, Wang X, Lin H, Ma F (2024) Disentangling ID and modality effects for session-based recommendation. In: Yang GH, Wang H, Han S, Hauff C, Zuccon G, Zhang Y (eds) Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024. ACM, pp 1883–1892

  36. Zhang X, Xu B, Yang L, Li C, Ma F, Liu H, Lin H (2022) Price DOES matter!: Modeling price and interest preferences in session-based recommendation. In: Amigó E, Castells P, Gonzalo J, Carterette B, Culpepper JS, Kazai G (eds) SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11–15, 2022. ACM, pp 1684–1693

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Funding

The work is supported by the National Natural Science Foundation of China under Grant No.61772342.

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Fan Yang: Conceptualization, Methodology, Formal analysis and investigation, Writing - original draft preparation, review and editing. Dunlu Peng: Conceptualization, Methodology, Formal analysis and investigation, supervision.

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Correspondence to Fan Yang.

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Yang, F., Peng, D. MVC-HGAT: multi-view contrastive hypergraph attention network for session-based recommendation. Appl Intell 55, 27 (2025). https://doi.org/10.1007/s10489-024-05877-1

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