Fully Utilizing Neighbors for Session-Based Recommendation with Graph Neural Networks

Abstract

Session-based recommendation (SBR) has attracted many researchers due to its highly practical value in many online services. Recently, graph neural networks (GNN) are widely applied to SBR due to their superiority on learning better item and session embeddings. However, existing GNN-based SBR models mainly leverage direct neighbors, lacking efficient utilization of multi-hop neighbors information. To address this issue, we propose a multi-head graph attention diffusion layer to utilize multi-hop neighbors information. Furthermore, we spot the information loss of local contextual aggregation in existing GNN-based models. To handle this problem, we propose positional graph attention aggregation layer to exploit direct neighbors information. Combining these two designs, we propose a novel model named FUN-GNN (Fully Utilizing Neighbors with Graph Neural Networks) for session-based recommendation. Experiments on three real datasets demonstrate its superiority over existing state-of-the-art baselines in terms of Precision and Mean Reciprocal Rank metrics.