  Conversational recommender systems (CRS) aim to recommend suitable items to
users through natural language conversations. For developing effective CRSs, a
major technical issue is how to accurately infer user preference from very
limited conversation context. To address issue, a promising solution is to
incorporate external data for enriching the context information. However, prior
studies mainly focus on designing fusion models tailored for some specific type
of external data, which is not general to model and utilize multi-type external
data.
  To effectively leverage multi-type external data, we propose a novel
coarse-to-fine contrastive learning framework to improve data semantic fusion
for CRS. In our approach, we first extract and represent multi-grained semantic
units from different data signals, and then align the associated multi-type
semantic units in a coarse-to-fine way. To implement this framework, we design
both coarse-grained and fine-grained procedures for modeling user preference,
where the former focuses on more general, coarse-grained semantic fusion and
the latter focuses on more specific, fine-grained semantic fusion. Such an
approach can be extended to incorporate more kinds of external data. Extensive
experiments on two public CRS datasets have demonstrated the effectiveness of
our approach in both recommendation and conversation tasks.
