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：在推荐系统设计中，针对物品提供者或最不满意用户实现公平性日益受到关注。受经济学不平等测量领域的启发，本文探索将广义基尼福利函数（GGFs）作为推荐系统应优化的规范性准则。GGFs根据个体在总体中的排序分配权重，对处境较差的个体赋予更高权重以促进平等。通过调整权重设置，GGFs既可最小化物品曝光度的基尼系数以促进物品间平等，也可聚焦于最不满意用户特定分位数上的表现。由于排序场景中的GGFs具有不可微性，这给优化带来挑战。我们利用非光滑优化工具和可微分排序中的投影算子解决了该问题。在包含高达1.5万用户与物品的真实数据集上进行的实验表明，相较基线方法，我们的方法在多种推荐任务和公平性准则下能实现更优的权衡。