Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations

• URL: http://arxiv.org/abs/2409.14399v2
• Date: Mon, 7 Oct 2024 07:49:27 GMT
• Title: Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations
• Authors: Peixin Qin, Chen Huang, Yang Deng, Wenqiang Lei, Tat-Seng Chua,
• Abstract summary: We propose a simple yet effective method, called PC-CRS, to enhance the credibility of CRS's explanations during persuasion. 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.
• Score: 63.05026345443155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender Systems [42.296965577732045]
  We discuss the concept of "sustainability-aware persuasive explanations" Based on a user study in three item domains, we analyze the potential impacts of sustainability-aware persuasive explanations.
  arXiv  Detail & Related papers  (2024-09-27T12:24:10Z)
• Stability of Explainable Recommendation [10.186029242664931]
  We study the vulnerability of existent feature-oriented explainable recommenders. We observe that all the explainable models are vulnerable to increased noise levels. Our study presents an empirical verification on the topic of robust explanations in recommender systems.
  arXiv  Detail & Related papers  (2024-05-03T04:44:51Z)
• User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations [96.45414741693119]
  We present a user-controllable recommender system that seamlessly integrates explainability and controllability. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system.
  arXiv  Detail & Related papers  (2023-08-02T01:13:36Z)
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models [115.7508325840751]
  The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs) In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
  arXiv  Detail & Related papers  (2023-05-22T15:12:43Z)
• EGCR: Explanation Generation for Conversational Recommendation [7.496434082286226]
  Explanation Generation for Conversational Recommendation (EGCR) based on generating explanations for conversational agents to explain why they make the action. EGCR incorporates user reviews to enhance the item representation and increase the informativeness of the whole conversation. We evaluate EGCR on one benchmark conversational recommendation datasets and achieve better performance on both recommendation accuracy and conversation quality than other state-of-the art models.
  arXiv  Detail & Related papers  (2022-08-17T02:30:41Z)
• Reinforced Path Reasoning for Counterfactual Explainable Recommendation [10.36395995374108]
  We propose a novel Counterfactual Explainable Recommendation (CERec) to generate item attribute-based counterfactual explanations. We reduce the huge search space with an adaptive path sampler by using rich context information of a given knowledge graph.
  arXiv  Detail & Related papers  (2022-07-14T05:59:58Z)
• Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion [52.49981085431061]
  Enhancing people's ability to write persuasive arguments could contribute to the effectiveness and civility in online communication. We derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions. Persua is an interactive visual system that provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments.
  arXiv  Detail & Related papers  (2022-04-16T08:07:53Z)
• Explainability in Music Recommender Systems [69.0506502017444]
  We discuss how explainability can be addressed in the context of Music Recommender Systems (MRSs) MRSs are often quite complex and optimized for recommendation accuracy. We show how explainability components can be integrated within a MRS and in what form explanations can be provided.
  arXiv  Detail & Related papers  (2022-01-25T18:32:11Z)
• KECRS: Towards Knowledge-Enriched Conversational Recommendation System [50.0292306485452]
  chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. external knowledge graphs (KG) have been introduced into chit-chat-based CRS. We propose the Knowledge-Enriched Conversational Recommendation System (KECRS) Experimental results on a large-scale dataset demonstrate that KECRS outperforms state-of-the-art chit-chat-based CRS.
  arXiv  Detail & Related papers  (2021-05-18T03:52:06Z)
• Why should I not follow you? Reasons For and Reasons Against in Responsible Recommender Systems [0.0]
  We argue that an RS can better enhance overall trust and transparency by frankly displaying both kinds of reasons to users. We have developed such an RS by exploiting knowledge graphs and by applying Snedegar's theory of practical reasoning.
  arXiv  Detail & Related papers  (2020-09-03T23:16:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.