Dynamic Multi-Interest Graph Neural Network for Session-Based Recommendation

Abstract

Session-based recommendation (SBR) is widely used in e-commerce and streaming services, with the task of performing real-time recommendations based on short-term anonymous user history data. Most existing SBR frameworks follow the pattern of learning a single representation for a specific session, which makes it difficult to capture potential multiple interests, thus preventing discriminative recommendation. Multi-Interest learning has emerged as an effective approach for addressing this issue on sequential data in recent years. However, the current Multi-Interest frameworks perform poorly on session data because they may generate excessive interests. To address these issues, we proposed a model named Dynamic Multi-Interest Graph Neural Network (DMI-GNN), which introduces the Multi-Interest learning framework into SBR and refines it by proposing a multiple positional patterns (MPP) learning method and a Dynamic Multi-Interest (DMI) regularization. Specifically, the MPP learning layer ensures the model to obtain representations with different positional information for sessions. The DMI regularization, on the other hand, mitigates the influence of excessive interests. Experiments on three bench-mark datasets demonstrate that our methods achieve better performance on different metrics.