mlsanityData to 7 Oct 2026

Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations

arXiv 2409.14399 ↗First posted 22 Sep 2024cs.CL cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 33 of its 66 references that are in the map. First posted on arXiv 22 Sep 2024.

Builds on

33 of 66 references.

References · 33

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
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  6. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 533
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  8. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 312
  9. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 207
  10. 2023FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Setscited by 198
  11. 2018Towards Deep Conversational Recommendationscited by 174
  12. 2020A Survey on Conversational Recommender Systemscited by 142
21 more
  1. 2023Human-like Summarization Evaluation with ChatGPTcited by 126
  2. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  3. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  4. 2021TopiOCQA: Open-domain Conversational Question Answering with Topic Switchingcited by 95
  5. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  6. 2021Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learningcited by 51
  7. 2024Towards Human-centered Proactive Conversational Agentscited by 48
  8. 2023Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generatorscited by 42
  9. 2024A Multi-Agent Conversational Recommender Systemcited by 42
  10. 2021Prevent the Language Model from being Overconfident in Neural Machine Translationcited by 34
  11. 2022A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systemscited by 32
  12. 2024Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM argumentscited by 30
  13. 2022Towards Improving Faithfulness in Abstractive Summarizationcited by 27
  14. 2020Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descentcited by 26
  15. 2022Evaluating Mixed-initiative Conversational Search Systems via User Simulationcited by 24
  16. 2022Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendationcited by 22
  17. 2022BARCOR: Towards A Unified Framework for Conversational Recommendation Systemscited by 21
  18. 2022User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systemscited by 20
  19. 2024STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agentscited by 17
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  21. 2024Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systemscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implicationscited by 30
  2. 2025Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Expertscited by 4
  3. 2025RecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systemscited by 1
  4. 2025Extracting Interaction-Aware Monosemantic Concepts in Recommender Systemscited by 0
  5. 2026Towards High-Level Semantic Intelligencecited by 0
  6. 2026Two Views, One Voice: Evidence-Grounded Conversational Music Recommendationcited by 0
  7. 2026The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shapecited by 0