论文标题
优化广义Gini指数以公平地排名
Optimizing generalized Gini indices for fairness in rankings
论文作者
论文摘要
在设计推荐系统的兴趣越来越大，旨在对物品生产商或其最不满意的用户进行公平。受经济学不平等测量领域的启发，本文探讨了通用的Gini福利功能（GGFS）的使用作为指定建议系统应优化的规范标准的手段。 GGFS体重个体取决于他们在人口中的排名，给较差的个体提供了更大的权重以促进平等。根据这些权重，GGFS最大程度地减少了项目暴露的Gini指数，以促进项目之间的平等，或者专注于最不满意的用户的特定分位数的性能。对于排名的GGF是挑战性优化的，因为它们是不可分割的。我们通过利用用于微分排序的非平滑优化和投影运算符的工具来解决这一挑战。我们使用最多15K用户和项目的真实数据集进行了实验，这表明我们的方法在各种推荐任务和公平标准上获得了比基线更好的权衡。
There is growing interest in designing recommender systems that aim at being fair towards item producers or their least satisfied users. Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. GGFs weight individuals depending on their ranks in the population, giving more weight to worse-off individuals to promote equality. Depending on these weights, GGFs minimize the Gini index of item exposure to promote equality between items, or focus on the performance on specific quantiles of least satisfied users. GGFs for ranking are challenging to optimize because they are non-differentiable. We resolve this challenge by leveraging tools from non-smooth optimization and projection operators used in differentiable sorting. We present experiments using real datasets with up to 15k users and items, which show that our approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.
