  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.
