  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.
