This paper aims to efficiently enable large language models (LLMs) to use
external knowledge and goal guidance in conversational recommender system (CRS)
tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks
for 1) generating grounded responses with recommendation-oriented knowledge, or
2) proactively leading the conversations through different dialogue goals. In
this work, we first analyze those limitations through a comprehensive
evaluation, showing the necessity of external knowledge and goal guidance which
contribute significantly to the recommendation accuracy and language quality.
In light of this finding, we propose a novel ChatCRS framework to decompose the
complex CRS task into several sub-tasks through the implementation of 1) a
knowledge retrieval agent using a tool-augmented approach to reason over
external Knowledge Bases and 2) a goal-planning agent for dialogue goal
prediction. Experimental results on two multi-goal CRS datasets reveal that
ChatCRS sets new state-of-the-art benchmarks, improving language quality of
informativeness by 17% and proactivity by 27%, and achieving a tenfold
enhancement in recommendation accuracy.

本文旨在高效地使大型语言模型在会话推荐系统任务中使用外部知识和目标引导。通过对先进的大型语言模型在特定领域的会话推荐系统任务中的局限性进行综合评估，我们发现外部知识和目标引导对于推荐准确性和语言质量有显著的贡献。基于这一发现，我们提出了一种新颖的 ChatCRS 框架，通过实现知识检索代理和目标规划代理，将复杂的会话推荐系统任务分解为多个子任务。在两个多目标会话推荐系统数据集上的实验结果表明，ChatCRS 建立了新的最先进基准，将信息的语言质量提高了 17%，主动性提高了 27%，并且推荐准确性提升了十倍。