Session-based recommendation aims to predict next item based on users’ anonymous behavior sequence within a short time. Recent studies focus on modeling sequential dependencies or complex relations among items in a session via recurrent/convolutional/graph neural networks. However, the following problems still remain: for short sessions, limited interactions cannot manifest user’s intent clearly; for long sessions, user’s interest may drift but be blurred by complex transitions. Motivated by the observation that different items are often belong to only a few categories or that closely related, in this article, we tackle these challenges by leveraging item category information, which is a concise form of knowledge and readily available in many platforms. We propose a novel method CaSe4SR that utilizes category sequence graph to augment session-based recommendation. In CaSe4SR, we build an item graph and a category graph, from user behavior sequence and item category sequence. The latter summarizes the former at concept level, which reduces item-level user behavior noises and makes user’s interest clearer. Afterwards, graph neural networks are applied on item graph and category graph respectively to learn representations of items and categories. Then two alternative fusion strategies and attention mechanism are designed to integrate them, yielding global embedding of the session, which is further combined with representation of last item to get ultimate session representation. Extensive experiments on real-world datasets show that CaSe4SR outperforms other state-of-the-art methods consistently. Detailed analysis reveals that category sequence graph is beneficial for next-item recommendation in sessions with different lengths.

基于会话的推荐旨在根据用户的匿名行为序列在短时间内预测下一个项目。最近的研究集中在通过循环/卷积/图形神经网络对会话中项目之间的顺序依赖性或复杂关系进行建模。但是，仍然存在以下问题：对于短时间会话，有限的交互无法清楚地表明用户的意图；对于长时间的会话，用户的兴趣可能会有所漂移，但会因复杂的过渡而变得模糊。由于观察到不同的项目通常仅属于少数几个类别或密切相关，因此在本文中，我们通过利用项目类别信息来解决这些挑战，项目类别信息是一种简明的知识形式，可在许多平台上轻松获得。我们提出了一种新颖的方法CaSe4SR，该方法利用类别序列图来增强基于会话的推荐。在CaSe4SR中，我们根据用户行为序列和项目类别序列构建项目图和类别图。后者在概念级别上总结了前者，从而减少了项目级别的用户行为噪音，并使用户的兴趣更加清晰。然后，将图神经网络分别应用于项目图和类别图，以学习项目和类别的表示。然后设计了两种替代融合策略和注意力机制，以将它们集成在一起，产生会话的全局嵌入，然后将其与最后一项的表示进一步组合以获得最终的会话表示。在现实世界的数据集上进行的大量实验表明，CaSe4SR始终优于其他最新技术。