Enhancing Session-Based Recommendation with Hypergraph Motifs and Contrastive Learning

Abstract

Session-based recommendation (SBR) provides personalized recommendations by analyzing the interactions of anonymous session users. Recent approaches based on graph neural networks (GNNs) focus on pairwise relations to infer potential user preferences. However, real-world user interactions are often complex, involving high-order relations that GNNs may not fully capture. Hypergraphs can naturally model high-order relations, while having untapped potential in SBR. In this paper, we propose a hypergraph convolutional network (MoHyNet) based on hypergraph motifs to improve SBR. Specifically, we construct a hypergraph convolution to extract high-order relations among users, thereby reducing the impact of intra-session irrelevant information on recommendations. Additionally, we introduce hypergraph motifs to characterize users’ behavioral patterns, thus enhancing inter-session information mining. Besides, we incorporate contrastive learning to strengthen the representation of the current session. Extensive experiments on multiple real-world datasets demonstrate the superiority of our proposed model over state-of-the-art approaches.