Session-based recommendation with fusion of hypergraph item global and context features

Session-based recommendation (SBR) is to predict the items that users are likely to click afterward by using their recent click history. Learning item features from existing session data to capture users’ current preferences is the main problem to be solved in session-based recommendation domain, and fusing global and local information to learn users’ preferences is an effective way to obtain this information more accurately. In this paper, we propose a session-based recommendation with fusion of hypergraph item global and context features (FHGIGC), which learns users’ current preferences by fusing item global and contextual features. Specifically, the model first constructs a global hypergraph and a local hypergraph and uses the hypergraph neural network to learn item global features and local features by relevant session information and item contextual information, respectively. Then, the learned features are fused by the attention mechanism to obtain the final item features and session features. Finally, personalized recommendations are generated for users based on the fused features. Experiments were conducted on three datasets of session-based recommendation, and the results demonstrate that the FHGIGC model can improve the accuracy of recommendations.