Abstract
Session-based recommendation (SBR) is the task of recommending the next item for users based on their short-term behavior sequences. Most of the current SBR methods model the transition patterns of items based on graph neural networks (GNNs) because of their ability to capture complex transition patterns. However, GNN-based SBR models neglect the global co-occurrence relationship among items and lack the ability to accurately model user intent due to limited evidence in sessions. In this paper, we propose a new SBR model based on Dual-channel Graph Representation Learning (called DCGRL), which well models user intent by capturing item relationships within and beyond sessions respectively. Specifically, we design a local-level hypergraph attention network to model multi-grained item transition relationships within a session by using sliding windows of different sizes. The experiments demonstrate the effectiveness and the efficiency of our proposed method compared with several state-of-the-art methods in terms of HR@20 and MRR@20.
This work was supported by the National Natural Science Foundation of China under Grant No. 62072084.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46 (2010)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Li, S., Xing, X., Liu, Y., Yang, Z., Niu, Y., Jia, Z.: Multi-preference book recommendation method based on graph convolution neural network. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds.) WISA 2022. LNCS, vol. 13579, pp. 521–532. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20309-1_46
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)
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)
Shani, G., Heckerman, D., Brafman, R.I., Boutilier, C.: An MDP-based recommender system. J. Mach. Learn. Res. 6(9) (2005)
Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 17–22 (2016)
Wang, J., Ding, K., Zhu, Z., Caverlee, J.: Session-based recommendation with hypergraph attention networks. In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 82–90. SIAM (2021)
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)
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)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, vol. 19, pp. 3940–3946 (2019)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 62072084, 62172082 and 62072086, the Science Research Funds of Liaoning Province of China under Grant No.LJKZ0094, the Natural Science Foundation of Liaoning Province of China under Grant No.2022-MS-171, the Science and Technology Program Major Project of Liaoning Province of China under Grant No.2022JH1/10400009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, X., Kou, Y., Shen, D., Nie, T., Li, D. (2023). Exploiting Item Relationships with Dual-Channel Attention Networks for Session-Based Recommendation. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-6222-8_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6221-1
Online ISBN: 978-981-99-6222-8
eBook Packages: Computer ScienceComputer Science (R0)