Abstract
Session-based recommendation(SBR) is a hot research direction, and how to solve the problem of sparse data in sessions is a challenging task. Although the existing model mirror graph enhanced neural model for session-based recommendation(MGS) solves the problem of data sparsity by introducing additional information from items, this model is still unable to model the general interest of the session well due to the limitations of data attribute information and the interference of noise, resulting in a certain amount of room for improvement in recommendation accuracy. To address this issue, this paper proposes a Global-Mirror Graph Model for Session-based Recommendation (GMGS), based on the research work related to MGS models, combining global graph modeling and reverse position awareness. Specifically, the GMGS model first builds a global graph based on all item transformations, and uses this to capture contextual information between sessions, thereby completing the modeling of general session interests. Then, a gating mechanism is used to combine reverse position information and the last click to obtain a session representation for final prediction. Finally, we conducted a large number of comparative experiments on three real datasets, and the GMGS model proposed in this paper achieved better results than the current representative recommendation models. Compared with the MGS model, the MRR@20 indicator has increased by 2.6\(\%\), 0.6\(\%\), and 6.5\(\%\) on the three datasets, respectively. In addition, we also conducted ablation experiments on three datasets to verify the effectiveness of each component improvement.
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References
Chen, J., Zheng, L., & Chen, S. (2023). User view dynamic graph-driven sequential recommendation. Knowledge and Information Systems, 65(6), 2541–2569. https://doi.org/10.1007/s10115-023-01840-7
Gu, J., Wang, Z., Kuen, J., et al. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354–377. https://doi.org/10.1016/j.patcog.2017.10.013
Guo, J., Yang, Y., Song, X., et al. (2022). Learning multi-granularity consecutive user intent unit for session-based recommendation. In: Proceedings of the fifteenth ACM International conference on web search and data mining, 343–352, https://doi.org/10.1145/3488560.3498524
Han, Q., Zhang, C., Chen, R., et al. (2022). Multi-faceted global item relation learning for session-based recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1705–1715, https://doi.org/10.1145/3477495.3532024
He, Y., Mao, Y., Xie, X., et al. (2022). An improved recommendation based on graph convolutional network. Journal of Intelligent Information Systems, 59(3), 801–823. https://doi.org/10.1007/s10844-022-00727-3
Hidasi, B., Karatzoglou, A., Baltrunas, L., et al. (2015). Session-based recommendations with recurrent neural networks. arXiv:1511.06939https://doi.org/10.48550/arXiv.1511.06939
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hsu, C., & Li, C. T. (2021). Retagnn: relational temporal attentive graph neural networks for holistic sequential recommendation. Proceedings of the web conference, 2021, 2968–2979. https://doi.org/10.1145/3442381.3449957
Jannach, D., Ludewig, M. (2017). When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the eleventh ACM conference on recommender systems, 306–310, https://doi.org/10.1145/3109859.3109872
Kang, W., McAuley, J. (2018). Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), 197–206, https://doi.org/10.1109/ICDM.2018.00035
Lai, S., Meng, E., Zhang. F., et al. (2022). An attribute-driven mirror graph network for session-based recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1674–1683, https://doi.org/10.1145/3477495.3531935
Li, J., Ren, P., Chen, Z., et al. (2017). Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 1419–1428, https://doi.org/10.1145/3132847.3132926
Li, Y., Zhan, Z., Li, H., et al. (2022). Interest-aware influence diffusion model for social recommendation. Journal of Intelligent Information Systems, 1–15. https://doi.org/10.1007/s10844-021-00684-3
Liu, C., Li, Y., Lin, H., et al. (2023). Gnnrec: Gated graph neural network for session-based social recommendation model. Journal of Intelligent Information Systems, 60(1), 137–156. https://doi.org/10.1007/s10844-022-00733-5
Liu, Q., Zeng, Y., Mokhosi, R., et al. (2018). Stamp: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 1831–1839, https://doi.org/10.1145/3219819.3219950
Luo, A., Zhao, P., Liu, Y., et al. (2020). Collaborative self-attention network for session-based recommendation. In: IJCAI, 2591–2597, https://doi.org/10.24963/ijcai.2020/355
Ma, C., Ma, L., Zhang, Y., et al. (2020). Memory augmented graph neural networks for sequential recommendation. In: Proceedings of the AAAI conference on artificial intelligence, 5045–5052, https://doi.org/10.1609/aaai.v34i04.5945
Pang, Y., Wu, L., Shen, Q., et al. (2022). Heterogeneous global graph neural networks for personalized session-based recommendation. In: Proceedings of the fifteenth ACM international conference on web search and data mining, 775–783, https://doi.org/10.1145/3488560.3498505
Qiu, R., Li, J., Huang, Z., et al. (2019). 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, 579–588, https://doi.org/10.1145/3357384.3358010
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L. (2010a). Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web, 811–820, https://doi.org/10.1145/1772690.1772773
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L. (2010b). Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web, 811–820, https://doi.org/10.1145/1772690.1772773
Shani, G., Heckerman, D., Brafman, R.I., et al. (2005). An mdp-based recommender system. Journal of Machine Learning Research 6(9)
Song, W., Xiao, Z., Wang, Y., et al. (2019). Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM international conference on web search and data mining, 555–563, https://doi.org/10.1145/3289600.3290989
Tang, J., Wang, K. (2018). Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining, 565–573, https://doi.org/10.1145/3159652.3159656
Wang, M., Ren, P., Mei, L., et al. (2019). A collaborative session-based recommendation approach with parallel memory modules. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, 345–354, 10.1145/3331184.3331210
Wang, Z., Wei, W., Cong, G., et al. (2020). 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, 169–178, 10.1145/3397271.3401142
Wu, S., Tang, Y., Zhu, Y., et al. (2019). Session-based recommendation with graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, 346–353, https://doi.org/10.1609/aaai.v33i01.3301346
Wu, Z., Pan, S., Chen, F., et al. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386
Xia, X., Yin, H., Yu, J., et al. (2021a). Self-supervised graph co-training for session-based recommendation. In: Proceedings of the 30th ACM International conference on information & knowledge management, 2180–2190, https://doi.org/10.1145/3459637.3482388
Xia, X., Yin, H., Yu, J., et al. (2021b). Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, 4503–4511, https://doi.org/10.1609/aaai.v35i5.16578
Xu, C., Zhao, P., Liu, Y., et al. (2019). Graph contextualized self-attention network for session-based recommendation. In: IJCAI, 3940–3946, https://doi.org/10.24963/ijcai.2019/547
Zhang, M., Wu, S., Yu, X., et al. (2022). Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2022.3151618
Zimdarsm, A., Chickering, D.M., Meek, C. (2013). Using temporal data for making recommendations. arXiv:1301.2320, https://doi.org/10.48550/arXiv.1301.2320
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Yuqiang Li: Methodology, Validation, Writing-original draft, Writing-review and editing. Jianxiang Long: Methodology, Validation, Writing-original draft, Writing-review and editing. Chun Liu: Validation, Writing-review and editing.
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Li, Y., Long, J. & Liu, C. Global-mirror graph network for session-based recommendation. J Intell Inf Syst 62, 255–272 (2024). https://doi.org/10.1007/s10844-023-00813-0
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DOI: https://doi.org/10.1007/s10844-023-00813-0