DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
This addresses the problem of improving recommendation accuracy for users in anonymous sessions, though it is incremental by extending existing graph-based approaches.
The paper tackles session-based recommendation by modeling item transitions not only within the target session but also across neighbor sessions to capture collaborative information, resulting in DGTN outperforming state-of-the-art methods on real-world datasets.
The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to capture item transitions within it, ignoring complex transitions between items in different sessions that have been generated by other users. These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session. In this paper, we propose a novel method, namely Dual-channel Graph Transition Network (DGTN), to model item transitions within not only the target session but also the neighbor sessions. Specifically, we integrate the target session and its neighbor (similar) sessions into a single graph. Then the transition signals are explicitly injected into the embedding by channel-aware propagation. Experiments on real-world datasets demonstrate that DGTN outperforms other state-of-the-art methods. Further analysis verifies the rationality of dual-channel item transition modeling, suggesting a potential future direction for session-based recommendation.