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English(EN) From Counterfactual Edits to Human Narratives: LLM-Driven Interpretations for Recommender System Explanations

LLM将复杂的推荐系统编辑转化为人类可读的解释

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种使用大型语言模型（LLM）将复杂反事实编辑列表转化为推荐系统可理解的自然语言解释的方法。这些LLM驱动的解释旨在通过总结更高级别的偏好模式来弥合原始解释数据与用户理解之间的差距。在MovieLens-1M数据集上的评估表明，这些摘要达到了很高的语言质量，并且像直接偏好优化（Direct Preference Optimization）这样的方法可以提高语义指标。 AI

影响 增强用户对AI驱动推荐的理解，可能增加信任和采用率。

排序理由 该集群包含详细介绍改进推荐系统解释新方法的学术论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

LLM将复杂的推荐系统编辑转化为人类可读的解释

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该集群包含详细介绍改进推荐系统解释新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Vojtech Cahlik, Amir Reza Mohammadi, Rodrigo Alves, Pavel Kordik, Eva Zangerle ·

    从反事实编辑到人类叙事：LLM驱动的推荐系统解释器

    arXiv:2611.13914v1 Announce Type: new Abstract: Recommender systems increasingly influence what users discover and consume online, yet the decision logic behind their outputs often remains opaque. Counterfactual explanation (CFX) methods provide an effective way to show which cha…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Eva Zangerle ·

    从反事实编辑到人类叙事：LLM驱动的推荐系统解释器

    Recommender systems increasingly influence what users discover and consume online, yet the decision logic behind their outputs often remains opaque. Counterfactual explanation (CFX) methods provide an effective way to show which changes to a user's interaction history would alter…

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