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交互，旨在满足用户即时信息需求并提供高质量推荐。尽管已有大量研究致力于开发有效的对话推荐系统，但大多数方法仍局限于当前对话中的上下文信息，常面临数据稀疏问题。因此，本文考虑利用历史对话数据来丰富当前对话会话的有限上下文。为此，我们提出一种新颖的多粒度超图兴趣建模方法，从不同视角捕捉复杂历史数据中的用户兴趣。核心思想在于利用超图表示历史对话中的复杂语义关系。具体而言，我们首先采用超图结构对用户历史对话会话进行建模，形成基于会话的超图，以捕获粗粒度的会话级关系；其次，为缓解数据稀疏问题，我们引入外部知识图谱，构建基于知识的超图，以建模细粒度的实体级语义。通过对这两种超图进行多粒度超图卷积，我们利用增强后的表示开发出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本方法在推荐与对话任务中的有效性。代码公开于：https://github.com/RUCAIBox/MHIM。