Tri-directional Hypergraph Contrastive Learning for Session-based Recommendation

Session-based recommendation (SBR) aims to forecast users' future actions by analyzing their unnamed behavioral sequences within a limited time frame. Recent research in SBR has focused on leveraging various techniques, including the incorporation of contrastive learning. Despite these developments, existing studies exhibit several limitations. First, these studies solely employ either item-level or session-level contrast, overlooking the vital correlation information between items and sessions. Second, to model the various relationships present in session data, numerous studies have designed complex processes to construct multiple augmented views, which diminish the accessibility of graph contrastive learning in SBR. To overcome these challenges, we propose Tri-Rec (Tri-directional Hypergraph Contrastive Learning for Session-based Recommendation), a model that innovatively incorporates tri-directional contrast into SBR. Tri- directional contrast consists of three distinct contrastive forms, with the aim of maximizing the similarity: (1) between the same item, (2) between the same session, and (3) between each session and its containing items in augmented views. Contrary to many prevailing methods that solely employ either item-level or session-level contrast, we not only utilize both but also introduce membership-level contrast, allowing the model to harness more comprehensive information. Furthermore, we integrate the hypergraph neural network and a self-attention based readout module to capture both high-order relationships and representative user intent among sessions. Detailed empirical evaluations conducted on three real-world datasets reveal that Tri-Rec markedly surpasses state-of-the-art approaches in performance.