Recently, there has been a rising awareness that when machine learning (ML)
algorithms are used to automate choices, they may treat/affect individuals
unfairly, with legal, ethical, or economic consequences. Recommender systems
are prominent examples of such ML systems that assist users in making
high-stakes judgments. A common trend in the previous literature research on
fairness in recommender systems is that the majority of works treat user and
item fairness concerns separately, ignoring the fact that recommender systems
operate in a two-sided marketplace. In this work, we present an
optimization-based re-ranking approach that seamlessly integrates fairness
constraints from both the consumer and producer-side in a joint objective
framework. We demonstrate through large-scale experiments on 8 datasets that
our proposed method is capable of improving both consumer and producer fairness
without reducing overall recommendation quality, demonstrating the role
algorithms may play in minimizing data biases.

本文提出了一种基于优化的重新排序方法，该方法在联合目标框架中无缝集成了来自消费者和生产者方面的公平性约束，并经过 8 个数据集的大规模实验证明，我们的方法能够提高消费者和生产者的公平性，同时不会降低总体的推荐质量。

CPFair：用于推荐系统的个性化消费者和生产者公平重排

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Many interesting problems in the Internet industry can be framed as a
two-sided marketplace problem. Examples include search applications and
recommender systems showing people, jobs, movies, products, restaurants, etc.
Incorporating fairness while building such systems is crucial and can have a
deep social and economic impact (applications include job recommendations,
recruiters searching for candidates, etc.). In this paper, we propose a
definition and develop an end-to-end framework for achieving fairness while
building such machine learning systems at scale. We extend prior work to
develop an optimization framework that can tackle fairness constraints from
both the source and destination sides of the marketplace, as well as dynamic
aspects of the problem. The framework is flexible enough to adapt to different
definitions of fairness and can be implemented in very large-scale settings. We
perform simulations to show the efficacy of our approach.

本文提出了一个端到端框架来实现市场机器学习系统的公平性，并扩展了先前的工作以开发一个优化框架，旨在解决市场的源端和目的端以及问题的动态方面的公平性约束，该框架足够灵活，以适应不同公平性定义，并可在非常大的规模环境中实现，并通过模拟展示了我们方法的有效性。