中文标题#
線與線之間：書籍推薦系統中主題偏見的研究
英文标题#
Reading Between the Lines: A Study of Thematic Bias in Book Recommender Systems
中文摘要#
推薦系統幫助用戶發現新內容，但也可能強化現有的偏見，導致不公平的曝光和多樣性減少。 本文介紹了並研究了書籍推薦中的主題偏見，即對某些書籍主題的不成比例的偏好或忽視。 我們採用多階段的偏見評估框架，使用 Book-Crossing 數據集來評估推薦中的主題偏見及其對不同用戶群體的影響。 我們的研究結果表明，主題偏見源於內容不平衡，並且被用戶參與模式所放大。 通過根據用戶的主題偏好對用戶進行分組，我們發現具有小眾和長尾興趣的用戶獲得的個性化推薦較少，而具有多樣化興趣的用戶獲得的推薦更加一致。 這些發現表明，推薦系統應仔細設計以適應更廣泛的用戶興趣。 通過為負責任的人工智能的總體目標做出貢獻，這項工作也為將主題偏見分析擴展到其他領域奠定了基礎。
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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