中文标题#
线与线之间：书籍推荐系统中主题偏见的研究
英文标题#
Reading Between the Lines: A Study of Thematic Bias in Book Recommender Systems
中文摘要#
推荐系统帮助用户发现新内容，但也可能强化现有的偏见，导致不公平的曝光和多样性减少。 本文介绍了并研究了书籍推荐中的主题偏见，即对某些书籍主题的不成比例的偏好或忽视。 我们采用多阶段的偏见评估框架，使用 Book-Crossing 数据集来评估推荐中的主题偏见及其对不同用户群体的影响。 我们的研究结果表明，主题偏见源于内容不平衡，并且被用户参与模式所放大。 通过根据用户的主题偏好对用户进行分组，我们发现具有小众和长尾兴趣的用户获得的个性化推荐较少，而具有多样化兴趣的用户获得的推荐更加一致。 这些发现表明，推荐系统应仔细设计以适应更广泛的用户兴趣。 通过为负责任的人工智能的总体目标做出贡献，这项工作也为将主题偏见分析扩展到其他领域奠定了基础。
英文摘要#
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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