Interpolating Item and User Fairness in Recommendation Systems

Online platforms employ recommendation systems to enhance customer engagement and drive revenue. However, in a multi-sided platform where the platform interacts with diverse stakeholders such as sellers (items) and customers (users), each with their own desired outcomes, finding an appropriate middle ground becomes a complex operational challenge. In this work, we investigate the ``price of fairness'', which captures the platform's potential compromises when balancing the interests of different stakeholders. Motivated by this, we propose a fair recommendation framework where the platform maximizes its revenue while interpolating between item and user fairness constraints. We further examine the fair recommendation problem in a more realistic yet challenging online setting, where the platform lacks knowledge of user preferences and can only observe binary purchase decisions. To address this, we design a low-regret online optimization algorithm that preserves the platform's revenue while achieving fairness for both items and users. Finally, we demonstrate the effectiveness of our framework and proposed method via a case study on MovieLens data.