Summary of Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations, by Peixin Qin et al.
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations
by Peixin Qin, Chen Huang, Yang Deng, Wenqiang Lei, Tat-Seng Chua
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, PC-CRS, aims to enhance the credibility of conversational recommender systems (CRS) by guiding explanation generation through credibility-aware persuasive strategies and refining them via post-hoc self-reflection. This approach addresses the issue of CRSs misinforming users with incredible explanations, ultimately damaging trust between users and the system. The method’s effectiveness is demonstrated through experimental results showing improved persuasion and credibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PC-CRS tries to make a conversational recommendation system (CRS) be more trustworthy by helping it explain things in a way that makes sense. Right now, these systems are really good at convincing people, but they can also make false claims to get people to do what they want. This is bad because it ruins the trust between people and the system. The new method, PC-CRS, tries to solve this by making the CRS think about what it’s saying before it says it. |
