Abstract
Session-based recommendation (SBR) aims to capture user intents based on a set of anonymous sessions for recommending the next item. Recent works in SBR often employ graph neural networks (GNNs) to model the transition patterns between items and have made impressive progress. However, the performance is still limited by data sparsity and complex dependency in sessions. Recently, self-supervised learning (SSL) has been applied in recommender systems because of its good ability to mine ground-truth samples from raw data, and great potential in relaxing data sparsity. We note that both sessions and individual items contain implicit user intents, and there is consistency between intents. Therefore, the SSL can be applied to construct self-supervised signals based on the implicit user intents to further alleviate the data sparsity problem in SBR, and thus improve the performance. In this paper, we propose a novel model called Intent Enhanced Self-Supervised Hypergraph Learning for session-based recommendation (ISHGL) to improve the performance. We first model the session sequence data as a global hypergraph to capture complex high-order relationships in sessions. Then, we devise a new contrastive method for self-supervised learning without additional data augmentation and complex positive/negative sample constructions. Extensive experiments on three datasets demonstrate the superiority of our model over the state-of-the-art methods.
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
Similar content being viewed by others
References
Bai, S., Zhang, F., Torr, P.H.: Hypergraph convolution and hypergraph attention. Pattern Recogn. 110, 107637 (2021)
Chen, J., Kou, G.: Attribute and structure preserving graph contrastive learning. In: AAAI, pp. 7024–7032 (2023)
Chen, T., Wong, R.C.W.: Handling information loss of graph neural networks for session-based recommendation. In: SIGKDD, pp. 1172–1180 (2020)
Han, J., et al.: DH-HGCN: dual homogeneity hypergraph convolutional network for multiple social recommendations. In: SIGIR, pp. 2190–2194 (2022)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: CIKM, pp. 1419–1428 (2017)
Li, Y., et al.: Enhancing hypergraph neural networks with intent disentanglement for session-based recommendation. In: SIGIR, pp. 1997–2002 (2022)
Liu, Q., et al.: STAMP: short-term attention/memory priority model for session-based recommendation. In: SIGKDD, pp. 1831–1839 (2018)
Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
Pan, Z., Cai, F., Chen, W., Chen, C., Chen, H.: Collaborative graph learning for session-based recommendation. TOIS 40(4), 1–26 (2022)
Pan, Z., Cai, F., Chen, W., Chen, H., De Rijke, M.: Star graph neural networks for session-based recommendation. In: CIKM, pp. 1195–1204 (2020)
Pang, Y., et al.: Heterogeneous global graph neural networks for personalized session-based recommendation. In: WSDM, pp. 775–783 (2022)
Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: CIKM, pp. 579–588 (2019)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811–820 (2010)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: WSDM, pp. 565–573 (2018)
Veličković, P., Fedus, W., Hamilton, W.L., Lió, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: ICLR (2019)
Wang, J., Ding, K., Zhu, Z., Caverlee, J.: Session-based recommendation with hypergraph attention networks. In: SDM, pp. 82–90 (2021)
Wang, Z., et al.: Global context enhanced graph neural networks for session-based recommendation. In: SIGIR, pp. 169–178 (2020)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: ICML, pp. 6861–6871 (2019)
Wu, J., et al.: Self-supervised graph learning for recommendation. In: SIGIR, pp. 726–735 (2021)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI, pp. 346–353 (2019)
Xia, X., et al.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: AAAI, pp. 4503-4511 (2021)
Xie, X., et al.: Contrastive learning for sequential recommendation. In: ICDE, pp. 1259–1273 (2022)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, pp. 3940–3946 (2019)
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: NeurIPS, vol. 33, pp. 5812–5823 (2020)
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., Huang, Z.: Self-supervised learning for recommender systems: a survey. TKDE (2023)
Yuan, J., Ji, W., Zhang, D., Pan, J., Wang, X.: Micro-behavior encoding for session-based recommendation. In: ICDE, pp. 2886–2899 (2022)
Zhou, K., et al.: S3-rec: self-supervised learning for sequential recommendation with mutual information maximization. In: CIKM, pp. 1893–1902 (2020)
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 Switzerland AG
About this paper
Cite this paper
Fang, X.S., Wu, Y., Lu, J., Gu, X., Sun, G., Zhan, Y. (2024). Intent Enhanced Self-supervised Hypergraph Learning for Session-Based Recommendation. In: Bifet, A., Krilavičius, T., Miliou, I., Nowaczyk, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14950. Springer, Cham. https://doi.org/10.1007/978-3-031-70381-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-70381-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70380-5
Online ISBN: 978-3-031-70381-2
eBook Packages: Computer ScienceComputer Science (R0)
