  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.
