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.

中文翻译：

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DGTN：用于基于会话的推荐的双通道图转换网络

基于会话的推荐任务是基于匿名会话预测用户行为。最近的研究主要将目标会话建模为序列或图形以捕获其中的项目转换，忽略其他用户生成的不同会话中的项目之间的复杂转换。这些项目转换包括潜在的协作信息并反映类似的行为模式，我们认为这可能有助于目标会话的推荐。在本文中，我们提出了一种新方法，即双通道图转换网络（DGTN），不仅可以对目标会话中的项目转换进行建模，还可以对相邻会话中的项目转换进行建模。具体来说，我们将目标会话和它的邻居（相似）会话集成到一个单一的图中。然后通过通道感知传播将转换信号显式地注入到嵌入中。在真实世界数据集上的实验表明，DGTN 优于其他最先进的方法。进一步的分析验证了双通道项目转换建模的合理性，为基于会话的推荐提供了一个潜在的未来方向。