Why didn't more people see it? Recommendation: Transparency for providers

2026-08-21

AI Summary

研究通过构建代理模型来解释推荐系统中内容提供者的项目曝光问题，量化特征贡献以揭示决策因素，提高透明度。

Abstract

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.

Problem

• transparency
• recommender systems
• item providers
• exposure

Innovation

• surrogate modeling
• item exposure
• recommender systems
• transparency