研究图书推荐中的主题偏见，揭示冷门题材用户更难获得个性化推荐。
Reading Between the Lines: A Study of Thematic Bias in Book Recommender Systems
- 基于书评数据构建多阶段偏见评估框架，分析主题偏好偏差
- 冷门主题用户获得的推荐更少，多元兴趣用户反而更易被精准推荐
- 为公平推荐系统设计提供依据，适用于内容平台治理
推荐系统帮助用户发现新内容，但也可能强化现有偏见，导致曝光不公和多样性下降。本文首次提出并研究图书推荐中的主题偏见，即对某些主题的过度偏好或忽视。基于Book-Crossing数据集，采用多阶段偏见评估框架，分析主题偏见的成因及其对不同用户群体的影响。研究发现，主题偏见源于内容分布不平衡，并被用户行为模式放大。按主题偏好划分用户后发现，具有小众和长尾兴趣的用户获得的个性化推荐更少，而兴趣多元的用户则获得更多一致推荐。结果表明，推荐系统需更包容多样兴趣。本工作推动负责任AI发展，也为其他领域扩展主题偏见分析奠定基础。
原文摘要 · Abstract (English)
Recommender systems help users discover new content, but can also reinforce existing biases, leading to unfair exposure and reduced diversity. This paper introduces and investigates thematic bias in book recommendations, defined as a disproportionate favouring or neglect of certain book themes. We adopt a multi-stage bias evaluation framework using the Book-Crossing dataset to evaluate thematic bias in recommendations and its impact on different user groups. Our findings show that thematic bias originates from content imbalances and is amplified by user engagement patterns. By segmenting users based on their thematic preferences, we find that users with niche and long-tail interests receive less personalised recommendations, whereas users with diverse interests receive more consistent recommendations. These findings suggest that recommender systems should be carefully designed to accommodate a broader range of user interests. By contributing to the broader goal of responsible AI, this work also lays the groundwork for extending thematic bias analysis to other domains.
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