用大模型生成推荐解释，提升系统透明度和用户信任。
On Explaining Recommendations with Large Language Models: A Review
- 用大模型自动生成推荐理由，替代传统规则或模板。
- 仅6篇论文直接研究该方向，领域仍处早期阶段。
- 适合对可解释推荐感兴趣的科研人员与产品设计者。
大型语言模型（如LLaMA和ChatGPT）的兴起为提升推荐系统的可解释性带来了新机遇。本文系统回顾了利用大模型生成推荐解释的研究进展——这是增强透明度与用户信任的关键环节。我们在ACM计算文献指南中进行了全面检索，覆盖从ChatGPT发布（2022年11月）至2024年11月的文献，共发现232篇相关文章，但经筛选后仅6篇直接探讨大模型在推荐解释中的应用。这一数量稀少表明，尽管大模型发展迅速，其在可解释推荐系统中的实际应用仍处于初期。我们分析了这6项研究的方法、挑战，并提出未来方向。结果表明，大模型有潜力显著改善推荐解释质量，呼吁构建更透明、以用户为中心的解释方案。
原文摘要 · Abstract (English)
The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations -- a critical aspect for fostering transparency and user trust. We conducted a comprehensive search within the ACM Guide to Computing Literature, covering publications from the launch of ChatGPT (November 2022) to the present (November 2024). Our search yielded 232 articles, but after applying inclusion criteria, only six were identified as directly addressing the use of LLMs in explaining recommendations. This scarcity highlights that, despite the rise of LLMs, their application in explainable recommender systems is still in an early stage. We analyze these select studies to understand current methodologies, identify challenges, and suggest directions for future research. Our findings underscore the potential of LLMs improving explanations of recommender systems and encourage the development of more transparent and user-centric recommendation explanation solutions.
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