Implicit user relationships across sessions enhanced graph for session-based recommendation

Abstract

Session-based recommendation aims to predict users’ next preference based on the sequence of their own history preferences in a short period. Most state-of-the-art methods model the session as a graph using graph neural networks (GNN) to capture the dynamic transitions between items within sessions. However, the complex and hidden correlations between different sessions are not adequately addressed, especially during the testing stage. We argue that session-based recommendation tasks can be improved by exploiting the correlations between different sessions in both training and testing. To this end, we propose a novel three-GNN-based recommendation framework to exploit the intra- and inter-session item correlations and the session-session correlations. The first one is a graph-based multi-layer perceptron to learn the inter-session item representations in a contrastive learning scheme guided by a contrastive loss function. The second one is a multi-relation graph attention network for intra-session item representations. The two item embeddings are combined through a position attention scheme to form the session representation, which is modulated and enhanced by the third extra session GNN by capturing the session-session correlations. The three levels of correlations are used in the training and testing stages in a joint prediction manner. To alleviate the data sparsity issue faced by the session GNN, we expand the session items by incorporating the neighboring items in the global item graph built from the entire training sessions. We have evaluated our method on multiple benchmark datasets. The results have shown that our joint recommendation method based on session correlation has significantly improved the recommendation accuracy over the state-of-the-art by more than 10%.