摘要
Next-item recommendation aims to predict users’ subsequent behaviors using their historical sequence data. However, sessions are often anonymous, short, and time-varying, making it challenging to capture accurate and evolving item representations. Existing methods using static graphs may fail to model the evolving semantics of items over time. To address this problem, we propose the Evolving Intra-session and Inter-session Graph Neural Network (EII-GNN) to capture the evolving item semantics by fusing global and local graph information. EII-GNN utilizes a global dynamic graph to model inter-session item transitions and update item embeddings at each timestamp. It also constructs a per-session graph with shortcut edges to learn complex intra-session patterns. To personalize recommendations, a history-aware GRU applies the user's past sessions. We fuse the inter-session graph, intra-session graph, and history embeddings to obtain the session representation for final recommendation. Our model performed well in experiments with three real-world data sets against its state-of-the-art counterparts.