Motif and supernode-enhanced gated graph neural networks for session-based recommendation

Abstract

Session-based recommendation systems aim to predict users’ next interactions based on short-lived, anonymous sessions, a challenging yet vital task due to the sparsity and dynamic nature of user behavior. Existing Graph Neural Network (GNN)-based methods primarily focus on the session graphs while overlooking the influence of micro-structures and user behavior patterns. To address these limitations, we propose a Motif and Supernode-Enhanced Session-based Recommender System (MSERS), which constructs a global session graph, identifies and encodes motifs as supernodes, and reintegrates them into the global graph to enrich its topology and better represent item dependencies. By employing supernode-enhanced Gated Graph Neural Networks (GGNN), MSERS captures both long-term and latent item dependencies, significantly improving session representations. Extensive experiments on two real-world datasets demonstrate the superiority of MSERS over baseline methods, providing robust insights into the role of micro-structures in session-based recommendations.