Parameter-Efficient Conversational Recommender System as a Language Processing Task
Mathieu Ravaut, Hao Zhang, Lu Xu, Aixin Sun, Yongxin Liu · 2024
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