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.

翻译：对话推荐系统旨在通过自然语言对话向用户推荐合适的物品。为开发高效的对话推荐系统，一个关键技术问题是如何从极其有限的对话上下文中准确推断用户偏好。针对该问题，引入外部数据以丰富上下文信息是一种有效的解决方案。然而，现有研究主要集中于针对特定类型外部数据设计融合模型，这类方法在处理多类型外部数据时缺乏通用性。为有效利用多类型外部数据，我们提出了一种新颖的由粗到细对比学习框架，以提升对话推荐系统的数据语义融合能力。该方法首先从不同数据信号中提取并表征多粒度语义单元，随后以由粗到细的方式对齐关联的多类型语义单元。为实现该框架，我们设计了粗粒度与细粒度两种用户偏好建模流程：前者侧重于通用粗粒度语义融合，后者聚焦于具体细粒度语义融合。该框架可扩展至融入更多类型的外部数据。在两类公开对话推荐系统数据集上的大量实验表明，本方法在推荐任务和对话任务中均取得了显著效果。