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.
翻译:在经济学中不平等计量领域的指导下,本文件探讨使用通用基尼福利功能(GGFs),作为确定建议系统应优化的规范标准的手段。 GGFs根据个人在人口中的级别对个人加权,给予处境最差的个人更多份量,以促进平等。根据这些加权,GGFs尽量减少项目暴露的基尼指数,以促进项目之间的平等,或注重最不满意用户的具体数量的业绩。 排名GGFs具有挑战性,因为其不具有差异性。我们通过利用非吸附优化和投影操作者的工具应对这一挑战。我们介绍利用多达15k用户和项目的实际数据集的实验,这些实验表明,我们的做法在各种建议任务和公平标准上比基线更有利取舍。