为内容创作者提供推荐系统曝光解释，揭示影响内容被推荐的关键因素。
Why didn't more people see it? Recommendation: Transparency for providers
- 用代理模型模拟推荐系统整体曝光分布
- 发现不同模型和领域中影响曝光的关键因素差异显著
- 适合关注推荐公平性与内容分发机制的研究者
推荐系统透明度研究多聚焦用户视角，但内容创作者在平台中的曝光情况却缺乏洞察。本文提出一种代理建模方法，从系统层面解释项目曝光分布。不针对单个用户-项目对，而是训练一个代理模型来近似推荐系统的整体曝光行为。通过量化各特征的贡献，揭示影响推荐决策的全局因素。在两个数据集和三种推荐模型上验证表明，代理模型能高保真地捕捉三类推荐器的全局行为，且关键影响因素在不同模型与领域间存在显著差异。
原文摘要 · Abstract (English)
Transparency in recommender systems has been widely studied from the perspective of those receiving recommendations, yet the needs of item providers, the creators whose content is distributed through these platforms, remain largely unexplored. Providers often lack insight into how their items do or do not receive exposure in users' recommendation lists. In this work, we address this gap by proposing a surrogate modeling approach to explain item exposure at a system level. Rather than explaining individual user-item pairs, we train a proxy model to approximate the exposure distribution produced by a recommender. By quantifying the contribution of each feature, we seek to explain the factors driving the recommendation model's decisions across the entire user base. We evaluate our approach on two datasets and three recommendation models. Results show that the surrogate model captures the global behavior of all three recommenders with high fidelity and that the most influential factors vary meaningfully across models and domains.
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