Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items' semantic information, a language model for dialogue generation, and a recommendation module for ranking relevant items. This combination of multiple components suffers from a cumbersome training process, and leads to semantic misalignment issues between dialogue generation and item recommendation. In this paper, we represent items in natural language and formulate CRS as a natural language processing task. Accordingly, we leverage the power of pre-trained language models to encode items, understand user intent via conversation, perform item recommendation through semantic matching, and generate dialogues. As a unified model, our PECRS (Parameter-Efficient CRS), can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. Experiments on two benchmark CRS datasets, ReDial and INSPIRED, demonstrate the effectiveness of PECRS on recommendation and conversation. Our code is available at: https://github.com/Ravoxsg/efficient_unified_crs.

翻译：对话推荐系统（CRS）旨在通过自然语言对话挖掘用户偏好，从而向用户推荐相关物品。现有研究通常结合外部知识图谱获取物品语义信息、语言模型生成对话、推荐模块对相关物品排序。这种多组件组合面临训练过程繁琐的问题，并导致对话生成与物品推荐之间的语义错位。本文通过将物品表示为自然语言形式，将CRS构造成一个自然语言处理任务。据此，我们利用预训练语言模型的能力对物品进行编码、通过对话理解用户意图、通过语义匹配执行物品推荐并生成对话。作为统一模型，我们提出的PECRS（参数高效CRS）可以在单一阶段进行优化，无需依赖知识图谱等非文本元数据。在ReDial和INSPIRED两个基准CRS数据集上的实验证明了PECRS在推荐与对话任务上的有效性。我们的代码已开源：https://github.com/Ravoxsg/efficient_unified_crs