Leveraging knowledge graph embedding for effective conversational recommendation - Xia, Yunwen (Nanyang Technological University, 2022)
Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to the 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 (KG)-based conversational recommender system (referred to 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 the 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 in terms of both the recommendation and conversation tasks.