Opportunistic Multi-aspect Fairness through Personalized Re-ranking
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
复制标题
通过个性化重新排名实现机会主义多方面公平
DOI：
10.1145/3340631.3394846
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Mobasher, Bamshad
中科院分区：
文献类型：
--
作者：
Sonboli, Nasim;Eskandanian, Farzad;Burke, Robin;Liu, Weiwen;Mobasher, Bamshad
随着推荐系统变得越来越普遍，并进入具有更大社会影响的领域，例如就业和住房，研究人员已经开始寻找方法来确保此类系统产生的结果的公平性。这项工作主要集中于开发推荐方法，其中公平性指标与推荐准确性共同优化。然而，之前的工作在很大程度上忽略了个人偏好如何限制算法产生公平推荐的能力。此外，除了少数例外，研究人员只考虑了相对于单个敏感特征或属性（例如种族或性别）来衡量公平性的场景。在本文中，我们提出了一种公平感知推荐的重新排序方法，该方法可以学习跨多个公平维度的个人偏好，并使用它们来增强推荐结果中的提供者公平性。具体来说，我们表明，与之前的重新排名方法相比，我们的机会主义和与度量无关的方法在准确性和公平性之间实现了更好的权衡，并且在多个公平性维度上做到了这一点。
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.
存在隐性偏见时的选择问题
DOI：
10.4230/lipics.itcs.2018.33
发表时间：
2018
期刊：
ArXiv
影响因子：
--
作者：
J. Kleinberg;Manish Raghavan
通讯作者：
Manish Raghavan