Abstract
Integrating side information in recommendation systems to address user feedback sparsity has gained significant research interest. However, existing models face challenges in generalization across different domains and types of side information. Specifically, two unresolved challenges are (1) the diverse formats of side information, including text sequences and numerical features, and (2) the challenge of measuring diverse correlations in side information beyond pairwise relationships. In this paper, we introduce \(\texttt {GENET} \) (Generalized hypErgraph pretraiNing on sidE informaTion), that pre-trains user and item representations on feedback-irrelevant side information and fine-tunes the representations on user feedback data. GENET utilizes pre-training to prevent side information from overshadowing critical feedback signals. It employs a hypergraph framework to accommodate various types of diverse side information. During pre-training, GENET integrates the task for hyperlink prediction by a unique strategy to enhance pre-training robustness by perturbing positive samples while maintaining high-order relations. Extensive experiments demonstrate that GENET exhibits strong generalization capabilities, outperforming the SOTA method by up to \(38\%\) in TOP-N recommendation on various datasets.
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
Notes
- 1.
https://github.com/XMUDM/GENET/
References
Fang, Y., Si, L.: Matrix co-factorization for recommendation with rich side information and implicit feedback. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems. pp. 65–69 (2011)
Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., Jin, D.: Deepmove: Predicting human mobility with attentional recurrent networks. In: WWW. pp. 1459–1468 (2018)
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI. pp. 3558–3565 (2019)
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI. vol. 33, pp. 3558–3565 (2019)
Gao, Y., Feng, Y., Ji, S., Ji, R.: Hgnn\(^{+}\): General hypergraph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In: SIGIR. pp. 639–648 (2020)
Li, H., Li, L., Xv, G., Lin, C., Li, K., Jiang, B.: Spex: A generic framework for enhancing neural social recommendation. TOIS pp. 1–33 (2021)
Liu, T., Wang, Z., Tang, J., Yang, S., Huang, G.Y., Liu, Z.: Recommender systems with heterogeneous side information. In: The world wide web conference. pp. 3027–3033 (2019)
Liu, Z., Yu, X., Fang, Y., Zhang, X.: Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. In: WWW. pp. 417–428 (2023)
Lu, Y., Jiang, X., Fang, Y., Shi, C.: Learning to pre-train graph neural networks. In: AAAI. vol. 35, pp. 4276–4284 (2021)
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR. pp. 43–52 (2015)
Pfadler, A., Zhao, H., Wang, J., Wang, L., Huang, P., Lee, D.L.: Billion-scale recommendation with heterogeneous side information at taobao. In: ICDE. pp. 1667–1676. IEEE (2020)
Rao, X., Chen, L., Liu, Y., Shang, S., Yao, B., Han, P.: Graph-flashback network for next location recommendation. In: KDD. pp. 1463–1471 (2022)
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: CIKM. pp. 1441–1450 (2019)
Tu, K., Cui, P., Wang, X., Wang, F., Zhu, W.: Structural deep embedding for hyper-networks. In: AAAI. vol. 32 (2018)
Wang, J., Huang, P., Zhao, H., Zhang, Z., Zhao, B., Lee, D.L.: Billion-scale commodity embedding for e-commerce recommendation in alibaba. In: KDD. pp. 839–848 (2018)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: SIGIR. pp. 165–174 (2019)
Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learning for recommendation. In: SIGIR. pp. 726–735 (2021)
Xia, L., Huang, C., Shi, J., Xu, Y.: Graph-less collaborative filtering. In: WWW. pp. 17–27 (2023)
Xia, L., Huang, C., Xu, Y., Zhao, J., Yin, D., Huang, J.: Hypergraph contrastive collaborative filtering. In: SIGIR
Yang, D., Fankhauser, B., Rosso, P., Cudre-Mauroux, P.: Location prediction over sparse user mobility traces using rnns. In: IJCAI. pp. 2184–2190 (2020)
Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C.: Modeling location-based user rating profiles for personalized recommendation. TKDD 9(3), 1–41 (2015)
Zhao, F., Xiao, M., Guo, Y.: Predictive collaborative filtering with side information. In: IJCAI. pp. 2385–2391 (2016)
Zhou, K., Wang, H., Zhao, W.X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., Wen, J.R.: S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In: CIKM. pp. 1893–1902 (2020)
Zhou, X.: A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation. arXiv preprint arXiv:2211.06924 (2022)
Acknowledgements
Chen Lin is the corresponding author. Chen Lin is supported by the National Key R&D Program of China (No. 2022ZD0160501), and the Natural Science Foundation of China (No.62372390)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y., Zhao, Q., Lin, C., Zhang, Z., Zhu, X., Su, J. (2025). GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14852. Springer, Singapore. https://doi.org/10.1007/978-981-97-5555-4_24
Download citation
DOI: https://doi.org/10.1007/978-981-97-5555-4_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5554-7
Online ISBN: 978-981-97-5555-4
eBook Packages: Computer ScienceComputer Science (R0)