_:n6921723ffdd64b259fcfb202929b506axb79
<http://schema.org/description>
"Session-based recommendation (SBR) focuses on next-item prediction at a\ncertain time point. As user profiles are generally not available in this\nscenario, capturing the user intent lying in the item transitions plays a\npivotal role. Recent graph neural networks (GNNs) based SBR methods regard the\nitem transitions as pairwise relations, which neglect the complex high-order\ninformation among items. Hypergraph provides a natural way to capture\nbeyond-pairwise relations, while its potential for SBR has remained unexplored.\nIn this paper, we fill this gap by modeling session-based data as a hypergraph\nand then propose a dual channel hypergraph convolutional network – DHCN to\nimprove SBR. Moreover, to enhance hypergraph modeling, we innovatively\nintegrate self-supervised learning into the training of our network by\nmaximizing mutual information between the session representations learned via\nthe two channels in DHCN, serving as an auxiliary task to improve the\nrecommendation task. Extensive experiments on three benchmark datasets\ndemonstrate the superiority of our model over the SOTA methods, and the\nablation study validates the effectiveness and rationale of hypergraph modeling\nand self-supervised task. The implementation of our model is available via\nhttps://github.com/xiaxin1998/DHCN."
<https://deepai.org/profile/lizhen-cui>