Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes between a protected and unprotected group, and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. We propose a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem. In particular, we express recommendation fairness as a novel combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. Simulations demonstrate the ability of the framework to integrate multiple fairness concerns in a dynamic way.

翻译：在个性化推荐的背景下，算法公平性所面临的挑战与分类任务中常见的问题存在显著差异。研究分类的学者通常将公平视为实现受保护群体与非受保护群体之间结果均等的问题，并据此构建算法干预措施。我们认为，现实应用场景中的公平性——尤其是在个性化推荐情境下——更为复杂且具有多面性，需要采用更通用的方法。我们提出一个模型，将推荐系统中的多利益相关方公平性形式化为两阶段社会选择问题。具体而言，我们将推荐公平性表达为分配问题与聚合问题的新颖组合，该组合融合了公平性关切与个性化推荐条款，并基于此推导出新的推荐技术。仿真实验表明，该框架能够以动态方式整合多重公平性关切。