Sequence-Aware Graph Neural Network for Session-based Recommendation

Abstract

Session-based recommendation (SBR) nowadays plays a vital role in many online services, aiming to predict users' next action based on anonymous sessions. Recent research of GNNs-based methods models a session as a graph via investigating complex transitions of items in a session. However, these methods do not consider sequential information of the session when aggregating item embeddings to form a session-level embedding. Most methods consider not all previous but the last one item as the interest of a user, which restricts the performance of the model. To address this problem, we propose a model named Sequence-Aware Graph Neural Network (SA-GNN) for session-based recommendation. In SA-GNN, we design a sequence-aware attention to adaptively weigh the previous items to generate a session-level embedding, which greatly improves the representation ability of the model. Also, to improve the representation ability of the item embeddings, SA-GNN harnesses the power of self-attention within the GNN layer to capture both transitions between adjacent items and long-range dependencies among all items in a session. In empirical evaluations on three public recommendation datasets, our method consistently outperforms an extensive of state-of-the-art session-based recommendation methods.