Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

Authors

  • Xin Xia The University of Queensland
  • Hongzhi Yin The University of Queensland
  • Junliang Yu The University of Queesland
  • Qinyong Wang The University of Queensland
  • Lizhen Cui ShanDong University
  • Xiangliang Zhang King Abdullah University of Science and Technology, Saudi Arabia

Keywords:

Recommender Systems & Collaborative Filtering

Abstract

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a dual channel hypergraph convolutional network -- DHCN to improve SBR. Moreover, to enhance hypergraph modeling, we innovatively integrate self-supervised learning into the training of our network by maximizing mutual information between the session representations learned via the two channels in DHCN, serving as an auxiliary task to improve the recommendation task. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the ablation study validates the effectiveness and rationale of hypergraph modeling and self-supervised task. The implementation of our model is available via https://github.com/xiaxin1998/DHCN.

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Published

2021-05-18

How to Cite

Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., & Zhang, X. (2021). Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4503-4511. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16578

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management