Semantic Information and Intention Enhanced Session-Based Recommendation With Contrastive Learning

Session-based recommendation (SBR) aims to predict upcoming user choices based on brief interaction histories. Over the past few years, graph neural networks (GNNs) have become a powerful tool for capturing intricate item relationships and delivering effective recommendations. Existing works use the sequential interactions within all sessions to construct graphs and provide self-supervised signals. Although some progresses have been made, we argue that merely relying on the transitions pattern fail to fully mine the complex information among items and lead to limited item representations. This article introduces a semantic information and intention enhanced SBR paradigm, which is called SISR. Our SISR leverages not only the sequential order of items but also the session intentions and semantic neighbors. Specifically, we begin by constructing a global item transition graph to enhance the GNN-based SBR with insights from items across all sessions. Then, the clustering mechanism is applied to obtain latent semantic prototypes of items and further extract the intention representations of sessions. Finally, we propose two contrastive learning component to distill the self-supervised signals for the item representation learning, so as to alleviate the data-sparsity phenomenon and augment the item recommendation component ratio. Comprehensive testing on three real-world datasets against various leading models highlights the advantages of our SISR paradigm.