用辩论式提示让推荐系统更清楚解释为何某项更合适
Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison
- 通过辩论式提示聚焦查询相关的项目对比点
- 在三个数据集上显著优于现有方法
- 适合需要理解推荐差异的用户或交互设计者
基于查询的推荐中，当面对未知项目时，用户难以理解为何某些项目更合适。查询驱动对比摘要（QCS）旨在利用项目描述的语言信息，澄清项目间的差异。然而，现有先进方法如STRUM-LLM仍存在不足。为此，本文提出Q-STRUM Debate，作为STRUM-LLM的扩展，采用辩论式提示生成与查询相关的、聚焦且具有对比性的项目特征摘要。借助现代大语言模型（LLMs）生成辩论过程，该方法提升了对比摘要质量。在三个数据集上的实验表明，Q-STRUM Debate在关键对比摘要指标上显著优于现有方法，展示了其作为新型高效辩论提示策略在QCS中的潜力。
原文摘要 · Abstract (English)
Query-driven recommendation with unknown items poses a challenge for users to understand why certain items are appropriate for their needs. Query-driven Contrastive Summarization (QCS) is a methodology designed to address this issue by leveraging language-based item descriptions to clarify contrasts between them. However, existing state-of-the-art contrastive summarization methods such as STRUM-LLM fall short of this goal. To overcome these limitations, we introduce Q-STRUM Debate, a novel extension of STRUM-LLM that employs debate-style prompting to generate focused and contrastive summarizations of item aspects relevant to a query. Leveraging modern large language models (LLMs) as powerful tools for generating debates, Q-STRUM Debate provides enhanced contrastive summaries. Experiments across three datasets demonstrate that Q-STRUM Debate yields significant performance improvements over existing methods on key contrastive summarization criteria, thus introducing a novel and performant debate prompting methodology for QCS.
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