MoHyNet: Enhancing Session-Based Recommendations via Hypergraph Motifs and Contrastive Learning

Session-based recommendation seeks to deliver personalized suggestions by decoding transient interaction sequences generated by anonymous users. Although graph neural networks have advanced this field by modeling pairwise item transitions, they fundamentally struggle to capture the complex, high-order dependencies inherent in real-world user behavior modeling. Consequently, while hypergraphs offer a natural mathematical solution for representing these multi-item relationships, existing approaches frequently overlook the localized structural semantics necessary to ground these abstract relations in physical browsing logic. To address these limitations, we introduce MoHyNet, a novel motif-guided hypergraph framework explicitly designed to capture both inter- and intra-session dependencies. By extracting localized hypergraph motifs, MoHyNet effectively decodes the recurring topological sub-structures and latent intentions behind anonymous interactions. Rather than treating hypergraphs merely as static representations of item co-occurrence, our approach utilizes these motifs as dynamic semantic filters to extract stable behavioral signatures from pseudo-sequential noise. To complement this intra-session modeling, we construct an augmented line graph that maps multi-hop dependencies across different sessions, employing neighborhood-aware convolutions to aggregate global collaborative context. A dual-view contrastive learning optimization is subsequently integrated to semantically align these intra-session structural signatures with the inter-session global context, ensuring a robust and holistic understanding of user intent. Extensive empirical evaluations on three real-world e-commerce datasets demonstrate that MoHyNet consistently outperforms state-of-the-art methods in session-based recommendation performance.