Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations

Understand

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.