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.

在个性化推荐背景下的算法公平性面临着与分类任务中不同的挑战。本文提出了一种多人利益相关社会选择问题的形式化模型，将推荐公平性表述为一个分配和聚合问题的结合，并提出了一些新的推荐技术。