用大模型过滤用户历史，让推荐解释更符合真实偏好。
Mitigating Popularity Bias in Counterfactual Explanations using Large Language Models
- 用大模型识别并剔除不符合用户性格的历史记录
- 在两个数据集上使解释更贴近用户真实偏好
- 适合关注推荐系统公平性与可解释性的研究者
反事实解释（CFE）通过展示用户行为微调后推荐结果的变化，提供可操作的推荐解释。然而，现有方法易受流行度偏差影响，生成与用户真实偏好不符的解释。本文提出在生成解释前，利用大语言模型预处理用户历史，过滤掉不契合用户特征的行为项。在两个公开数据集上的实验表明，将该方法应用于神经反事实解释框架ACCENT，可生成更贴合用户流行度偏好的反事实解释，显著提升解释相关性。
原文摘要 · Abstract (English)
Counterfactual explanations (CFEs) offer a tangible and actionable way to explain recommendations by showing users a "what-if" scenario that demonstrates how small changes in their history would alter the system's output. However, existing CFE methods are susceptible to bias, generating explanations that might misalign with the user's actual preferences. In this paper, we propose a pre-processing step that leverages large language models to filter out-of-character history items before generating an explanation. In experiments on two public datasets, we focus on popularity bias and apply our approach to ACCENT, a neural CFE framework. We find that it creates counterfactuals that are more closely aligned with each user's popularity preferences than ACCENT alone.
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