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.

翻译：在线平台利用推荐系统提升用户参与度并驱动收入增长。然而在多边平台中，平台需要与卖家（物品）和顾客（用户）等不同利益相关者互动，各方都有各自的期望目标，因此寻找恰当的平衡点成为复杂的运营挑战。本研究聚焦"公平代价"这一概念，该指标衡量平台在平衡不同利益相关者利益时可能做出的权衡。受此启发，我们提出一个公平推荐框架，使平台在最大化收入的同时，对物品公平约束与用户公平约束进行插值处理。我们进一步在更真实但更具挑战性的在线场景中研究公平推荐问题，该场景下平台缺乏用户偏好信息，仅能观测到二元购买决策。为解决这一难题，我们设计了一种低遗憾在线优化算法，该算法在保障平台收入的同时实现物品与用户的公平性。最后，通过基于MovieLens数据的案例研究验证了所提框架与方法的有效性。