论推荐系统中偏差研究的挑战：一项UserKNN案例研究
On the challenges of studying bias in Recommender Systems: A UserKNN case study
AI总结：
该研究以UserKNN为案例，通过合成数据与不同算法配置的对比实验，揭示了数据属性和实现配置会影响推荐系统流行度偏差的测量结论，强调偏差研究需明确说明算法配置与数据特性。
摘要：
关于推荐系统偏差传播的论述往往难以证实或证伪。偏差研究通常从少量公开可用数据集取材，因此受限于这些数据集的特定属性。此外，研究中的实现选择往往未被明确描述或说明其动机，而这些选择可能对偏差传播产生影响。本文探讨了测量与报告流行度偏差的挑战。我们通过将合成数据与实现了UserKNN的知名推荐系统框架相结合，展示了数据属性与算法配置对流行度偏差的影响。首先，我们基于UserKNN的功能特性，识别出可能影响流行度偏差的数据特征，并据此生成了结合这些特征的多种数据集。其次，我们找出了文献中不同实现存在差异的UserKNN配置。我们针对5个合成数据集和5种UserKNN配置评估了流行度偏差，并就它们的联合作用提供了见解。研究发现，根据数据特征的不同，不同的UserKNN配置可能会得出关于流行度偏差传播的不同结论。这些结果表明，在报告和解释推荐系统中的偏差时，有必要明确说明算法配置与数据属性。
英文摘要：
Statements on the propagation of bias by recommender systems are often hard to verify or falsify. Research on bias tends to draw from a small pool of publicly available datasets and is therefore bound by their specific properties. Additionally, implementation choices are often not explicitly described or motivated in research, while they may have an effect on bias propagation. In this paper, we explore the challenges of measuring and reporting popularity bias. We showcase the impact of data properties and algorithm configurations on popularity bias by combining synthetic data with well known recommender systems frameworks that implement UserKNN. First, we identify data characteristics that might impact popularity bias, based on the functionality of UserKNN. Accordingly, we generate various datasets that combine these characteristics. Second, we locate UserKNN configurations that vary across implementations in literature. We evaluate popularity bias for five synthetic datasets and five UserKNN configurations, and offer insights on their joint effect. We find that, depending on the data characteristics, various UserKNN configurations can lead to different conclusions regarding the propagation of popularity bias. These results motivate the need for explicitly addressing algorithmic configuration and data properties when reporting and interpreting bias in recommender systems.
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