Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

309 Scopus citations


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 sessionbased data as a hypergraph and then propose a hypergraph convolutional network to improve SBR. Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task. Since the two types of networks both are based on hypergraph, which can be seen as two channels for hypergraph modeling, we name our model DHCN (Dual Channel Hypergraph Convolutional Networks). Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the resultsvalidate the effectiveness of hypergraph modeling and selfsupervised task. The implementation of our model is available via
Original languageEnglish (US)
Title of host publicationThe Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)
Number of pages9
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-07
Acknowledgements: This work was supported by ARC Discovery Project (GrantNo.DP190101985, DP170103954).


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