让推荐系统在说服用户时更可信，避免胡编乱造的解释
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations
- 用可信度感知策略引导生成解释，再通过事后自省优化
- 实验表明新方法既说服力强又更可信，还能提升推荐准确率
- 适合关注长期用户信任、追求真实解释的推荐系统研究者
借助大语言模型，当前对话式推荐系统（CRS）具备了强大的说服能力。然而，这些系统常在解释中掺杂不可信信息，损害用户长期信任。为此，我们提出PC-CRS方法，通过可信度感知的说服策略引导解释生成，并利用事后自省逐步优化解释内容。实验验证了该方法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原因，并证明可信解释有助于提高推荐准确性。
原文摘要 · Abstract (English)
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.
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