Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to traditional recommender system, it learns the user preference better through interactions (i.e. conversations), and then further boosts the recommendation performance. However, existing studies on CRS ignore to address the relationship among attributes, users, and items effectively, which might lead to inappropriate questions and inaccurate recommendations. In this view, we propose a knowledge graph based conversational recommender system (referred as KG-CRS). Specifically, we first integrate the user-item graph and item-attribute graph into a dynamic graph, i.e., dynamically changing during the dialogue process by removing negative items or attributes. We then learn informative embedding of users, items, and attributes by also considering propagation through neighbors on the graph. Extensive experiments on three real datasets validate the superiority of our method over the state-of-the-art approaches in terms of both the recommendation and conversation tasks.

翻译：对话推荐系统（CRS）融合了对话系统与推荐系统的技术，近年来受到日益广泛的关注。与传统推荐系统相比，它通过交互（即对话）更有效地学习用户偏好，从而进一步提升推荐性能。然而，现有CRS研究未能有效处理属性、用户与项目之间的关联关系，这可能导致不恰当的提问与不准确的推荐。为此，本文提出一种基于知识图谱的对话推荐系统（简称KG-CRS）。具体而言，我们首先将用户-项目图与项目-属性图整合为动态图谱——即在对话过程中通过移除负面项目或属性实现动态演化。随后，通过考虑图中邻居节点的传播信息，学习用户、项目与属性的语义嵌入表示。在三个真实数据集上的大量实验表明，本方法在推荐任务与对话任务方面均优于当前最先进的方法。