Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user's instant information need. Although great efforts have been made to develop effective CRS, most of them still focus on the contextual information from the current dialogue, usually suffering from the data scarcity issue. Therefore, we consider leveraging historical dialogue data to enrich the limited contexts of the current dialogue session. In this paper, we propose a novel multi-grained hypergraph interest modeling approach to capture user interest beneath intricate historical data from different perspectives. As the core idea, we employ hypergraph to represent complicated semantic relations underlying historical dialogues. In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations. Second, to alleviate the issue of data scarcity, we use an external knowledge graph and construct a knowledge-based hypergraph considering fine-grained, entity-level semantics. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to develop interest-aware CRS. Extensive experiments on two benchmarks ReDial and TG-ReDial validate the effectiveness of our approach on both recommendation and conversation tasks. Code is available at: https://github.com/RUCAIBox/MHIM.

翻译：对话推荐系统通过与用户进行多轮自然语言对话，为用户即时信息需求提供高质量推荐。尽管已有大量研究致力于开发有效的对话推荐系统，但多数方法仍聚焦于当前对话的上下文信息，常面临数据稀疏性问题。为此，我们提出利用历史对话数据来丰富当前对话会话的有限上下文。本文提出一种新颖的多粒度超图兴趣建模方法，从不同角度挖掘复杂历史数据中隐藏的用户兴趣。核心思路是采用超图表示历史对话中的复杂语义关系。具体而言，我们首先利用超图结构建模用户历史对话会话，构建基于会话的超图以捕获粗粒度的会话级关系；其次，为缓解数据稀疏性问题，引入外部知识图谱并构建基于知识的超图，以建模细粒度的实体级语义。接着，我们对这两类超图执行多粒度超图卷积，利用增强后的表示开发兴趣感知的对话推荐系统。在ReDial和TG-ReDial两个基准数据集上的大量实验验证了该方法在推荐和对话任务中的有效性。代码已开源至：https://github.com/RUCAIBox/MHIM。