超越说服：迈向具备可信解释的对话推荐系统
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations
AI总结：
针对现有对话推荐系统解释易含不可信信息、损害用户长期信任的问题，提出PC-CRS方法，通过可信度感知说服策略和事后自反思生成兼具说服力与可信度的解释，验证了其有效性及可信解释对推荐准确率的提升潜力。
摘要：
在大语言模型的助力下，当前的对话推荐系统（conversational recommender system，CRS）已具备较强的说服用户接受推荐物品的能力。这类CRS虽说服力强，但可能在解释中加入不可信信息误导用户，最终损害用户与CRS间的长期信任。为解决该问题，我们提出一种简单有效的方法PC-CRS，用于在说服过程中提升CRS解释的可信度。它通过我们提出的可信度感知说服策略引导解释生成，再通过事后自反思逐步优化解释。实验结果证明了PC-CRS在生成兼具说服力与可信度的解释方面的有效性。进一步分析揭示了现有方法生成不可信解释的原因，以及可信解释提升推荐准确率的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
