论文标题
DGTN：基于会话建议的双通道图跃迁网络
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
论文作者
论文摘要
基于会话建议的任务是根据匿名会话预测用户操作。最近的研究主要将目标会话模拟为序列或图形，以捕获其中的项目过渡，而忽略了其他用户生成的不同会话中项目之间的复杂过渡。这些项目的转变包括潜在的协作信息并反映相似的行为模式，我们认为这可能有助于对目标会话的建议。在本文中，我们提出了一种新颖的方法，即双通道图跃迁网络（DGTN），不仅在目标会话中，而且在邻居会话中建模项目过渡。具体而言，我们将目标会话及其邻居（相似）会话集成到单个图中。然后，通过通道感知传播将过渡信号明确注入嵌入到嵌入中。现实世界数据集的实验表明，DGTN优于其他最新方法。进一步的分析验证了双通道项目过渡建模的合理性，这表明了基于会话建议的潜在未来方向。
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.
