A Hypergraph Framework Based on Neighborhood and Category Association Relationships for Session-Based Recommendation

Session-based recommendation aims to generate personalized recommendations based on user behaviors within a browsing session. The traditional graph neural network (GNN) methods focus on modeling pair-wise relationships but ignore neighborhood and category relationships which is crucial for improving the accuracy of recommendations. This paper proposes a hypergraph framework based on neighborhood and category association relationships for session-based recommendation (HLNCA). In detail, we first construct a hypergraph that can acquire the neighborhood and category association relationships among items; then aggregate hyperedge representation to obtain the neighborhood and category relationships of nodes; and finally obtain the users' preference through the attention mechanism to calculate the predicted score of items in the candidate set according to the preference. The proposed HL-NCA model outperforms baseline models in terms of Recall@20 and MRR@20 evaluation metrics.