CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
This addresses fairness issues in two-sided recommender systems, which is important for users and producers, but is incremental as it builds on existing re-ranking methods.
The paper tackled the problem of fairness in recommender systems by proposing an optimization-based re-ranking approach that integrates both consumer and producer fairness constraints in a joint framework, demonstrating through experiments on 8 datasets that it improves fairness without reducing recommendation quality.
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.