提出公平重排序方法，多维度缓解推荐系统偏见
Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems
- 利用已有偏见修正跨群体推荐差异
- 在多个敏感属性上降低性别、年龄等偏见
- 保持性能基本不变，适合实际部署
推荐系统在电商、求职、娱乐等领域深刻影响用户体验。为避免不公平推荐，本文提出一种公平感知的重排序方法，旨在缓解不同物品类别中的偏见问题。以往工作多关注二元敏感属性，忽略类别内偏见；本文则同时处理性别、年龄、职业等多维敏感属性。该方法通过挖掘现有偏见来矫正推荐不均衡。在三个真实数据集上的实验表明，该方案能有效降低社会偏见，且性能损失极小。
原文摘要 · Abstract (English)
Recommendation systems play a crucial role in our daily lives by impacting user experience across various domains, including e-commerce, job advertisements, entertainment, etc. Given the vital role of such systems in our lives, practitioners must ensure they do not produce unfair and imbalanced recommendations. Previous work addressing bias in recommendations overlooked bias in certain item categories, potentially leaving some biases unaddressed. Additionally, most previous work on fair re-ranking focused on binary-sensitive attributes. In this paper, we address these issues by proposing a fairness-aware re-ranking approach that helps mitigate bias in different categories of items. This re-ranking approach leverages existing biases to correct disparities in recommendations across various demographic groups. We show how our approach can mitigate bias on multiple sensitive attributes, including gender, age, and occupation. We experimented on three real-world datasets to evaluate the effectiveness of our re-ranking scheme in mitigating bias in recommendations. Our results show how this approach helps mitigate social bias with little to no degradation in performance.
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