With the aid of large language models, current conversational recommender system (CRS) has gaining strong abilities to persuade users to accept recommended items. While these CRSs are highly persuasive, they can mislead users by incorporating incredible information in their explanations, ultimately damaging the long-term trust between users and the CRS. To address this, we propose a simple yet effective method, called PC-CRS, to enhance the credibility of CRS's explanations during persuasion. It guides the explanation generation through our proposed credibility-aware persuasive strategies and then gradually refines explanations via post-hoc self-reflection. Experimental results demonstrate the efficacy of PC-CRS in promoting persuasive and credible explanations. Further analysis reveals the reason behind current methods producing incredible explanations and the potential of credible explanations to improve recommendation accuracy.

翻译：借助大型语言模型，当前对话式推荐系统（CRS）已具备强大的说服用户接受推荐项目的能力。尽管这些CRS具有高度说服力，但其解释中可能包含不可信信息，从而误导用户，最终损害用户与CRS之间的长期信任关系。为解决此问题，我们提出一种名为PC-CRS的简洁而有效的方法，以增强CRS在说服过程中解释的可信度。该方法通过我们提出的可信度感知说服策略引导解释生成，并随后通过事后自反思机制逐步优化解释。实验结果证明了PC-CRS在提升解释说服力与可信度方面的有效性。进一步分析揭示了现有方法产生不可信解释的内在原因，并证实了可信解释对提高推荐准确性的潜在价值。