Parameter-Efficient Conversational Recommender System as a Language Processing Task
CoRR(2024)
摘要
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.
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