We tackle the challenge of integrating large language models (LLMs) with external recommender systems to enhance domain expertise in conversational recommendation (CRS). Current LLM-based CRS approaches primarily rely on zero/few-shot methods for generating item recommendations based on user queries, but this method faces two significant challenges: (1) without domain-specific adaptation, LLMs frequently recommend items not in the target item space, resulting in low recommendation accuracy; and (2) LLMs largely rely on dialogue context for content-based recommendations, neglecting the collaborative relationships among item sequences. To address these limitations, we introduce the CARE (Contextual Adaptation of Recommenders) framework. CARE (a) integrates external recommender systems as domain experts, producing candidate items through entity-level insights, and (b) customizes LLMs as rerankers to enhance the accuracy by leveraging contextual information. Our results demonstrate that incorporating CARE framework significantly enhances recommendation accuracy of LLMs by an average of 54% and 25% for ReDial and INSPIRED datasets. The most effective CARE strategy involves LLMs selecting and reranking candidate items that external recommenders provide based on contextual insights.

翻译：我们解决了将大型语言模型（LLM）与外部推荐系统集成以增强对话推荐（CRS）领域专业知识的挑战。当前基于LLM的CRS方法主要依赖零/少样本方法根据用户查询生成物品推荐，但这一方法面临两大挑战：（1）缺乏领域特异性适应时，LLM频繁推荐目标物品空间外的物品，导致推荐准确率低下；（2）LLM主要依赖对话上下文进行基于内容的推荐，忽视了物品序列间的协同关系。为解决这些局限，我们引入CARE（推荐系统上下文适应）框架。CARE (a) 将外部推荐系统作为领域专家集成，通过实体级洞察生成候选物品，以及(b)将LLM定制为重排序器，利用上下文信息提升准确率。结果表明，采用CARE框架后，LLM在ReDial和INSPIRED数据集上的推荐准确率平均分别提升54%和25%。最优的CARE策略使LLM基于上下文洞察对外部推荐系统提供的候选物品进行选择与重排序。