Abstract
Session-based recommendation systems aim to predict the next user interaction based on the items with which the user interacts in the current session. Currently, graph neural network-based models have been widely used and proven more effective than others. However, these session-based models mainly focus on the user-item and item-item relations in historical sessions while ignoring information shared by similar users. To address the above issues, a new graph-based representation, User-item Group Graph, which considers not only user-item and item-item but also user-user relations, is developed to take advantage of natural sequential relations shared by similar users. A new personalized session-based recommendation model is developed based on this representation. It first generates groups according to user-related historical item sequences and then uses a user group preference recognition module to capture and balance between group-item preferences and user-item preferences. Comparison experiments show that the proposed model outperforms other state-of-art models when similar users are effectively grouped. This indicates that grouping similar users can help find deep preferences shared by users from the same group and is instructive in finding the most appropriate next item for the current user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, T., Wong, R.C.W.: 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 (2020). https://doi.org/10.1145/3394486.3403170
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015). https://doi.org/10.48550/arXiv.1511.06939
Lai, S., Meng, E., Zhang, F., Li, C., Wang, B., Sun, A.: 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, pp. 1674–1683 (2022). https://doi.org/10.1145/3477495.3531935
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017). https://doi.org/10.1145/3132847.3132926
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015). https://doi.org/10.48550/arXiv.1511.05493
Liu, F., Cheng, Z., Zhu, L., Gao, Z., Nie, L.: Interest-aware message-passing gcn for recommendation. In: Proceedings of the Web Conference 2021, pp. 1296–1305 (2021). https://doi.org/10.1145/3442381.3449986
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: 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, pp. 1831–1839 (2018). https://doi.org/10.1145/3219819.3219950
Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 825–833 (2019). https://doi.org/10.1145/3292500.3330984
Pan, Z., Cai, F., Chen, W., Chen, H., De Rijke, M.: Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1195–1204 (2020). https://doi.org/10.1145/3340531.3412014
Pang, Y., et al.: Heterogeneous global graph neural networks for personalized session-based recommendation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 775–783 (2022). https://doi.org/10.1145/3488560.3498505
Qiu, R., Li, J., Huang, Z., Yin, H.: 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 (2019). https://doi.org/10.1145/3357384.3358010
Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130–137 (2017). https://doi.org/10.1145/3109859.3109896
Ren, P., Chen, Z., Li, J., Ren, Z., Ma, J., De Rijke, M.: RepeatNet: a repeat aware neural recommendation machine for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4806–4813 (2019). https://doi.org/10.1609/aaai.v33i01.33014806
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010). https://doi.org/10.1145/1772690.1772773
Shani, G., Brafman, R.I., Heckerman, D.: An MDP-based recommender system. arXiv e-prints arXiv:1301.0600 (2012)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018). https://doi.org/10.1145/3159652.3159656
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. (CSUR) 54(7), 1–38 (2021). https://doi.org/10.1145/3465401
Wang, Z., Wei, W., Cong, G., Li, X.L., Mao, X.L., Qiu, M.: 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 (2020). https://doi.org/10.1145/3397271.3401142
Wu, F., Zhang, T., Holanda de Souza A., Jr., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. arXiv e-prints arXiv:1902.07153 (2019)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019). https://doi.org/10.1609/aaai.v33i01.3301346
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI International Joint Conference on Artificial Intelligence, p. 3940 (2019). https://doi.org/10.24963/ijcai.2019/547
Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., Wang, L.: Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Trans. Knowl. Data Eng. 34(8), 3946–3957 (2020). https://doi.org/10.1109/TKDE.2020.3031329
Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320 (2013)
Acknowledgements
The work is supported by the National Natural Science Foundation of China (No. 41871286) and the 1331 Engineering Project of Shanxi Province, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, H., Bai, H., Huo, J., Yang, M. (2024). A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_26
Download citation
DOI: https://doi.org/10.1007/978-981-99-8082-6_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8081-9
Online ISBN: 978-981-99-8082-6
eBook Packages: Computer ScienceComputer Science (R0)