  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.
