2024/08/02 by Yunwen Xia, Xia, Yunwen, Hui Fang +5
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Embedding #FOS: Computer and information sciences #Graph #Information Retrieval (cs.IR) #Intelligent Tutoring Systems and Adaptive Learning #Knowledge graph #Theoretical computer science #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2408.01342
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/08/02 · openalex created_date 2025/01/13 · openalex updated_date 2026/07/28
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.