In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness. We introduce \emph{inferiority}, a novel (un)fairness measure quantifying a user's competitive disadvantage for their recommended items. Inferiority complements \emph{envy}, a fairness notion measuring preference for others' recommendations. We combine inferiority and envy with \emph{utility}, an accuracy-related measure of aggregated relevancy scores. Since these measures are non-differentiable, we reformulate them using a probabilistic interpretation of recommender systems, yielding differentiable versions. We combine these loss functions in a multi-objective optimization problem called \texttt{FEIR} (Fairness through Envy and Inferiority Reduction), applied as post-processing for standard recommender systems. Experiments on synthetic and real-world data demonstrate that our approach improves trade-offs between inferiority, envy, and utility compared to naive recommendations and the baseline methods.

翻译：摘要：在电子招聘和在线约会等场景中，推荐涉及有限机会的分配，因此需要新的方法来量化和实施公平性。我们引入“劣势”这一新型（不）公平性度量，用于量化用户因推荐项目而产生的竞争劣势。劣势是对“嫉妒”（一种衡量用户对他人推荐偏好程度的公平性概念）的补充。我们将劣势和嫉妒与“效用”（一种基于聚合相关性分数的准确性相关度量）相结合。由于这些度量不可微，我们利用推荐系统的概率解释对其重新表述，得到可微版本。我们将这些损失函数组合成一个名为FEIR（通过减少嫉妒与劣势实现公平）的多目标优化问题，作为标准推荐系统的后处理步骤。在合成数据和真实世界数据上的实验表明，与朴素推荐和基线方法相比，我们的方法在劣势、嫉妒和效用之间的权衡方面取得了改进。