Global Context-Aware Graph Neural Networks for Session-based Recommendation

Session-based recommendation, which uses the interactive information of anonymous users in a period to predict user preferences, has attracted extensive attention. Existing methods mainly exploit the strengths of graph neural networks (GNNs) in capturing structured data features to model complex item transitions for recommendations. However, there still remain some challenges: 1) many methods neglect to exploit cross-session interactions and fail to integrate the latent intra- and inter-session information effectively. 2) some methods barely explore fine-grained contextual factors underlying in the sessions for session-based recommendation, thus failing to capture more comprehensive user preferences. To solve these issues, we propose a novel method called Global Context-Aware Graph Neural Networks (GCA-GNN) which captures the local-session and cross-session preferences respectively, and captures fine-grained global contextual factors to complement user preferences. Specifically, GCA-GNN models user preferences from two different views: the cross-session view and the local-session view. The former learns collaborative user preferences from a cross-session graph, while the latter is designed to learn users' personal preferences from local-session graphs. Furthermore, a context-aware Capsule Graph Neural Network is employed to extract fine-grained contextual factors, serving as complementary information. And we introduce an auxiliary self-supervised learning task to enhance user preferences. Experiments on benchmark datasets demonstrate the strength of our model over the state-of-the-art methods.