CGG: Category-aware global graph contrastive learning for session-based recommendation

Abstract

With the auxiliary role of category information in capturing user interests, employing category information to improve session-based recommendation (SBR) is getting an energetic research point. Recent studies organized the category-aware session as the graph structure and utilized the graph neural network to explore the session interest for SBR. However, existing studies only focused on the category information in the current session and failed to overcome inherent sparsity of session data, which resulted in suboptimal SBR performance. To overcome these deficiencies, we propose a Category-aware Global Graph contrastive learning method, namely CGG, for SBR. To be specific, we firstly construct the category-aware global graph based on global item-item transitions, item-category associations and global category-category transitions, which utilizes more sufficient category information across sessions to learn embeddings of categories and items. Furthermore, we design the hierarchical dual-pattern contrastive learning mechanism to model the information interaction of graphical and sequential patterns of a category-aware session, which overcomes the negative influence of sparse session data by injecting self-supervised signals. Extensive experiments on multiple real-world datasets verify that CGG outperforms seven mainstream SBR methods on different measurements.