AI 中文总结
针对推荐系统中项目提供者的曝光透明度研究，提出代理建模方法解释系统级项目曝光，在两个数据集和三个推荐模型上验证了该方法的高保真度及因素的领域与模型差异性。
AI 中文摘要
推荐系统的透明度已从推荐接收者的角度得到广泛研究，但项目提供者（即通过这些平台分发内容的创作者）的需求在很大程度上未被探索。提供者往往不了解自己的项目为何能或不能在用户的推荐列表中获得曝光。在本研究中，我们通过提出一种代理建模方法来解决这一空白，以在系统层面解释项目曝光情况。我们不解释单个用户-项目对，而是训练一个代理模型来近似推荐系统生成的曝光分布。通过量化每个特征的贡献，我们旨在解释推荐模型在全体用户中的决策驱动因素。我们在两个数据集和三个推荐模型上评估了我们的方法。结果表明，该代理模型以高保真度捕捉了所有三个推荐模型的全局行为，且最具影响力的因素会随模型和领域发生有意义的变化。
英文摘要
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.
