研究发现推荐系统会放大热门内容偏见，冷门内容更难被推荐。
Investigating Popularity Bias Amplification in Recommender Systems Employed in the Entertainment Domain
- 分析音乐、电影、动漫三类数据，发现推荐频次与热度正相关
- 偏好冷门内容的用户获得的推荐准确率显著更低
- 揭示推荐精度、校准质量与热门偏见间的内在关联
推荐系统通过分析用户历史行为，在音乐、电影和书籍等娱乐领域提供内容推荐，已成为人工智能和机器学习最广泛应用之一。随着欧盟《人工智能法案》等可信AI规范出台，公平性与偏见问题愈发重要。本文聚焦于娱乐领域中的流行度偏见——即热门内容在推荐列表中被过度呈现，而冷门内容被忽视。通过对音乐、电影、动漫三个领域的数据集分析，发现项目被推荐的频率与其流行度呈正相关。结果表明，对热门内容兴趣较低的用户群体所获得的推荐准确性明显低于偏好热门内容的用户。此外，本研究深化了对推荐准确率、算法校准质量与流行度偏见放大之间关系的理解。
原文摘要 · Abstract (English)
Recommender systems have become an integral part of our daily online experience by analyzing past user behavior to suggest relevant content in entertainment domains such as music, movies, and books. Today, they are among the most widely used applications of AI and machine learning. Consequently, regulations and guidelines for trustworthy AI, such as the European AI Act, which addresses issues like bias and fairness, are highly relevant to the design, development, and evaluation of recommender systems. One particularly important type of bias in this context is popularity bias, which results in the unfair underrepresentation of less popular content in recommendation lists. This work summarizes our research on investigating the amplification of popularity bias in recommender systems within the entertainment sector. Analyzing datasets from three entertainment domains, music, movies, and anime, we demonstrate that an item's recommendation frequency is positively correlated with its popularity. As a result, user groups with little interest in popular content receive less accurate recommendations compared to those who prefer widely popular items. Furthermore, this work contributes to a better understanding of the connection between recommendation accuracy, calibration quality of algorithms, and popularity bias amplification.
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