Category-aware self-supervised graph neural network for session-based recommendation

Session-based recommendation which focuses on predicting the next behavior according to anonymous sessions of behavior records plays an important role in real-world applications. Most previous session-based recommendation approaches capture the preferences of users by modeling the behavior records between users and items within current session. However, items’ category information is not fully exploited, while existing works are still suffering from the severe issue of data sparsity. In this work, we propose a novel session-based recommendation model, namely Category-aware Self-supervised Graph Neural Network (namely CSGNN), which adopts a pre-training layer for capturing the features of items and categories, as well as the correlations among them. Especially, we build a category-aware heterogeneous hypergraph composed of item nodes and category nodes, which enhances the information learning in the current session. Then we design item-level and category-level self-attention models to represent the information of item and category, respectively, and integrate global and local preference of user for session-based recommendation. Finally, we combine self-supervised learning by constructing a category-aware session graph to further enhance the performance CSGNN and alleviate the data sparsity problem. Comprehensive experiments are conducted on three real-world datasets, Nowplaying, Diginetica, and Tmall, and the results show that the proposed model CSGNN achieves better performance than session-based recommendation baselines with several state-of-the-art approaches.