  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.
