KGCL: A Knowledge-enhanced Graph Contrastive learning framework for session-based recommendation

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Session-based Recommendation (SBR) aims at predicting the next item based on a short-term anonymous user behavior, whose main challenge lies at the sparsity problem of user–item​ interactions. Graph contrastive learning, which discovers ground-truth samples by data augmentation, is a promising paradigm to tackle this problem. However, the following two insights are often overlooked by most of these contrastive learning-based models. First, item knowledge (i.e., item attributes, which can be distilled from open knowledge graphs) provides side information to model the complex high-order relations among items. Second, effective embedding aggregating mechanism is capable of filtering noisy preference signals (i.e., unrelated items) in sessions and retaining higher weight for the related items. These insights motivate us to construct an item attribute hypergraph to summarize associations among items that share common attributes and develop a Knowledge-enhanced Graph Contrastive Learning framework for session-based recommendation (KGCL). Technically, KGCL constructs two independent and complementary views (cross-session graph and item attribute hypergraph) in terms of user–item interactions sequence and item intrinsic attributes respectively, so as to explicitly capture both internal and external factors of items. Then, we encode item and session embeddings with a query-aware graph attention network and a hypergraph convolutional network over the above two views. Finally, we devise two contrastive learning loss — global–global contrastive learning and local–global contrastive learning — that maximize agreement between these two views and generate high-quality recommendation results. Extensive experiments conducted on three real-world datasets show KGCL has a higher expressive power that enables SBR to predict the next item.