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TGIE4REC: enhancing session-based recommendation with transition and global information

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Abstract

Predicting the next most likely interactive item based on the current session is the goal of session-based recommendation (SBR). In order to model the adjacent item transition information from previous session sequences and the current session sequences, the most advanced techniques in SBR use graph neural networks and attention mechanisms. Position-aware attention is used to incorporate the reversed position information in an item in order to learn the importance of each item in the session when generating the session representation. However, these methods have certain drawbacks. First, using data from previous sessions always introduces uncorrelated items (noise). Second, learning the sequence transition relations between items in the session sequence is challenging due to reverse position coding. This study presents a novel SBR technique called TGIE4Rec. Specifically, TGIE4Rec learns two levels of session embedding, global information enhanced session embedding and transition information enhanced session embedding. The global information enhanced session representation learning layer employs the information of other sessions and the current session to learn global-level session embedding, and the transition information enhanced session representation learning layer employs the items of the current session to learn new session embedding and integrates the time information into the item representation in the session sequence for neighbor embedding learning, so as to further enhance the sequential transition relations in the session sequence. Experiments on three benchmark datasets have demonstrated that TGIE4Rec is superior to other advanced methods.

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Data availability

The Diginetica data that support the findings of this study are available from “Codalab”,“https://competitions.codalab.org/competitions/11161” The Tmall data that support the findings of this study are available from “Tiannchi”, “https://tianchi.aliyun.com/dataset/dataDetail?dataId=42” The Nowplaying data that support the findings of this study are available from “DBIS”, “http://dbis-nowplaying.uibk.ac.at/#nowplaying”

References

  1. Liu Y, Ma H, Jiang Y, Li Z (2022) Modelling risk and return awareness for p2p lending recommendation with graph convolutional networks. Appl Intell 52:1–16

    Google Scholar 

  2. Jiang Y, Ma H, Zhang X, Li Z, Chang L (2022) An effective two-way metapath encoder over heterogeneous information network for recommendation. In: Proceedings of the 2022 International Conference on Multimedia Retrieval, pp 90–98

  3. Singer U, Roitman H, Eshel Y, Nus A, Guy I, Levi O, Hasson I, Kiperwasser E (2022) Sequential modeling with multiple attributes for watchlist recommendation in e-commerce. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp 937–946

  4. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp 285–295

  5. Hidasi BH, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations, pp 1–10

  6. Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: AAAI, pp 346–353

  7. Wang Z, Wei W, Cong G, Li XL, Mao X, Qiu M (2020) Global context enhanced graph neural networks for session-based recommendation. In: SIGIR, pp 169–178

  8. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: WWW, pp 811–820

  9. Jing L, Ren P, Chen Z, Ren Z, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1419–1428

  10. Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) Stamp: short-term attention/memory priority model for session-based recommendation. SIGKDD explorations, pp 1831–1839

  11. Yuan J, Song Z, Sun M, Wang X, Zhao WX (2021) Dual sparse attention network for session-based recommendation. In: Thirty-Fifth Conference on Artificial Intelligence, AAAI 2021, pp 4635–4643

  12. Li Y, Tarlow D, Brockschmidt M, Zemel RS (2016) Gated graph sequence neural networks. In: Bengio Y, LeCun Y (eds) ICLR (Poster)

  13. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw (TNN) 20(1):61–80

    Article  Google Scholar 

  14. Kipf TN, Welling M (2017) semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. ICLR ’17

  15. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2017) Graph attention networks. In: ICLR 2018

  16. Ding C, Zhao Z, Li C, Yu Y, Zeng Q (2023) Session-based recommendation with hypergraph convolutional networks and sequential information embeddings. Expert Syst Appl 223:119875

    Article  Google Scholar 

  17. Wang J, Xie H, Wang FL, Lee L-K, Wei M (2023) Jointly modeling intra-and inter-session dependencies with graph neural networks for session-based recommendations. Inf Process Manag 60(2):103209

    Article  Google Scholar 

  18. Hou Y, Hu B, Zhang Z, Zhao WX (2022) Core: simple and effective session-based recommendation within consistent representation space. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1796–1801

  19. Wang M, Ren P, Mei L, Chen Z, Ma J, de Rijke M (2019) A collaborative session-based recommendation approach with parallel memory modules. In: SIGIR, pp 345–354

  20. Luo A, Zhao P, Liu Y, Zhuang F, Wang D, Xu J, Fang J, Sheng VS (2020) Collaborative self-attention network for session-based recommendation. In: IJCAI, pp 2591–2597

  21. Feng L, Cai Y, Wei E, Li J (2022) Graph neural networks with global noise filtering for session-based recommendation. Neurocomputing 472:113–123

    Article  Google Scholar 

  22. Kang WC, McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), pp 197–206

  23. Wang H, Liu G, Liu A, Li Z, Zheng K (2019) DMRAN: a hierarchical fine-grained attention-based network for recommendation. In: IJCAI, pp 3698–3704

  24. Wang S, Hu L, Wang Y, Sheng QZ, Orgun MA, Cao L (2019) Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In: IJCAI, pp 3771–3777

  25. Qiu R, Li J, Huang Z, Yin H (2019) Rethinking the item order in session-based recommendation with graph neural networks. In: CIKM, pp 579–588

  26. Xu C, Zhao P, Liu Y, Sheng VS, Xu J, Zhuang F, Fang J, Zhou X (2019) Graph contextualized self-attention network for session-based recommendation. In: IJCAI, pp 3940–3946

  27. Pan Z, Cai F, Chen W, Chen H, De Rijke M (2020) Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 1195–1204

  28. Zhu G, Hou H, Chen J, Yuan C, Huang Y (2022) Transition relation aware self-attention for session-based recommendation. CoRR abs/2203.06407

  29. 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 

  30. Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 4503–4511

  31. Xia X, Yin H, Yu J, Shao Y, Cui L (2021) Self-supervised graph co-training for session-based recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp 2180–2190

  32. Zhang Z, Yang B, Xu H, Hu W (2024) Multi-level category-aware graph neural network for session-based recommendation. Expert Syst Appl 242:122773

  33. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  Google Scholar 

  34. Ye W, Wang S, Chen X, Wang X, Qin Z, Yin D (2020) Time matters: sequential recommendation with complex temporal information. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, pp 1459–1468

  35. Yang Z, Ding M, Xu B, Yang H, Tang J (2022) Stam: a spatiotemporal aggregation method for graph neural network-based recommendation. In: The ACM Web Conference 2022, Virtual Event, pp 3217–3228

  36. Zangerle E, Pichl M, Gassler W, Specht GS (2014) Nowplaying music dataset: extracting listening behavior from twitter. In: Proceedings of the First International Workshop on Internet-Scale Multimedia Management, pp 21–26

  37. Lai S, Meng E, Zhang F, Li C, Wang B, Sun A (2022) An attribute-driven mirror graph network for session-based recommendation. In: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1674–1683

  38. Zangerle E, Pichl M, Gassler W, Specht GS (2014) Nowplaying music dataset: extracting listening behavior from twitter. In: Proceedings of the First International Workshop on Internet-Scale Multimedia Management, pp 21–26

  39. Sheng Z, Zhang T, Zhang Y, Gao S (2023) Enhanced graph neural network for session-based recommendation. Expert Syst Appl 213:118887

    Article  Google Scholar 

  40. Chen Q, Jiang F, Guo X, Chen J, Sha K, Wang Y (2024) Combine temporal information in session-based recommendation with graph neural networks. Expert Syst Appl 238:121969

    Article  Google Scholar 

  41. Pan Z, Cai F, Chen W, Chen C, Chen H (2022) Collaborative graph learning for session-based recommendation. ACM Trans Inf Syst 40(4):72–17226

    Google Scholar 

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Acknowledgements

The authors would like to thank the associate editor and the reviewers for their useful feedback that improved this paper, and thank to the providers of the public datasets used in this manuscript.

Funding

This work is supported by the Natural Science Foundation of Gansu Province (Grant No.22JR5RA156), the National Natural Science Foundation of China (Grant No.62341314) and the Backbone Fund of Youth Teachers’ Capability Promotion (Grant No.NWNU-LKQN2020–14).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Shiwei Gao, Jingyu Wang, Yufeng Zeng & Xiaohui Dong

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  1. Shiwei Gao
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  2. Jingyu Wang
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  3. Yufeng Zeng
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  4. Xiaohui Dong
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Contributions

SG contributed to conceptualization and methodology. JW helped in software and writing—original draft preparation. YZ helped in reviewing and editing. XD helped in writing.

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Correspondence to Shiwei Gao.

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Gao, S., Wang, J., Zeng, Y. et al. TGIE4REC: enhancing session-based recommendation with transition and global information. J Supercomput 80, 11585–11613 (2024). https://doi.org/10.1007/s11227-024-05897-1

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