Multi-View Graph Contrastive Neural Networks for Session-Based Recommendation

Abstract

Session-based recommendation (SBR) aims to predict the next item a user may interact with based on an anonymous session, playing a crucial role in real-time recommendation scenarios. However, existing SBR models struggle to effectively capture local session dependencies and global item relationships, while also facing challenges such as data sparsity and noisy information interference. To address these challenges, this paper proposes a novel Multi-View Graph Contrastive Learning Neural Network (MVGCL-GNN), which enhances recommendation performance through multi-view graph modeling and contrastive learning. Specifically, we construct three key graph structures: a session graph for modeling short-term item dependencies, a global item graph for capturing cross-session item transitions, and a global category graph for learning category-level relationships. In addition, we introduce simple graph contrastive learning to improve embedding quality and reduce noise interference. Furthermore, a soft attention mechanism is employed to effectively integrate session-level and global-level information representations. Extensive experiments conducted on two real-world datasets demonstrate that MVGCL-GNN consistently outperforms state-of-the-art baselines. MVGCL-GNN achieves 34.96% in P@20 and 16.50% in MRR@20 on the Tmall dataset, and 22.59% in P@20 and 8.60% in MRR@20 on the Nowplaying dataset. These results validate the effectiveness of multi-view graphs and contrastive learning in improving both accuracy and robustness for session-based recommendation tasks.