A Large Language Model Enhanced Conversational Recommender System
Yue Feng Shuchang Liu Zhenghai Xue Qingpeng Cai Lantao Hu Peng Jiang Kun Gai Fei Sun
Conversational recommender systems (CRSs) aim to recommend high-quality items
to users through a dialogue interface. It usually contains multiple sub-tasks,
such as user preference elicitation, recommendation, explanation, and item
information search. To develop effective CRSs, there are some challenges: 1)
how to properly manage sub-tasks; 2) how to effectively solve different
sub-tasks; and 3) how to correctly generate responses that interact with users.
Recently, Large Language Models (LLMs) have exhibited an unprecedented ability
to reason and generate, presenting a new opportunity to develop more powerful
CRSs. In this work, we propose a new LLM-based CRS, referred to as LLMCRS, to
address the above challenges. For sub-task management, we leverage the
reasoning ability of LLM to effectively manage sub-task. For sub-task solving,
we collaborate LLM with expert models of different sub-tasks to achieve the
enhanced performance. For response generation, we utilize the generation
ability of LLM as a language interface to better interact with users.
Specifically, LLMCRS divides the workflow into four stages: sub-task detection,
model matching, sub-task execution, and response generation. LLMCRS also
designs schema-based instruction, demonstration-based instruction, dynamic
sub-task and model matching, and summary-based generation to instruct LLM to
generate desired results in the workflow. Finally, to adapt LLM to
conversational recommendations, we also propose to fine-tune LLM with
reinforcement learning from CRSs performance feedback, referred to as RLPF.
Experimental results on benchmark datasets show that LLMCRS with RLPF
outperforms the existing methods.