复现研究发现儿童推荐算法表现差异源于年龄本质差异与数据偏差。
Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
- 在电影、音乐、书籍多领域复现儿童推荐差异
- 揭示儿童与主流用户消费模式存在跨域一致性差异
- 适合关注儿童数字权益与推荐公平性的研究者
儿童常被推荐算法所影响，但现有研究极少将儿童作为独立用户群体，且多基于儿童样本不足的数据集，易忽视其真实兴趣，偏向主流用户偏好。近期研究发现儿童消费行为与主流用户不同，导致推荐算法对儿童表现不一致。本文在电影、音乐、书籍三个领域扩展复现该研究，覆盖更广泛数据集，揭示跨域一致的儿童-推荐系统互动模式，以及特定数据集或领域独有的特征。同时引入流行度偏差指标深化原研究分析。结果表明，儿童与成人间的推荐差异部分源于内在年龄差异，部分则受具体数据分布影响。
原文摘要 · Abstract (English)
Children are often exposed to items curated by recommendation algorithms. Yet, research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users. Recently, Ungruh et al. demonstrated that children's consumption patterns and preferences differ from those of mainstream users, resulting in inconsistent recommendation algorithm performance and behavior for this user group. These findings, however, are based on two datasets with a limited child user sample. We reproduce and replicate this study on a wider range of datasets in the movie, music, and book domains, uncovering interaction patterns and aspects of child-recommender interactions consistent across domains, as well as those specific to some user samples in the data. We also extend insights from the original study with popularity bias metrics, given the interpretation of results from the original study. With this reproduction and extension, we uncover consumption patterns and differences between age groups stemming from intrinsic differences between children and others, and those unique to specific datasets or domains.
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