Today's online platforms rely heavily on algorithmic recommendations to bolster user engagement and drive revenue. However, such algorithmic recommendations can impact diverse stakeholders involved, namely the platform, items (seller), and users (customers), each with their unique objectives. In such multi-sided platforms, finding an appropriate middle ground becomes a complex operational challenge. Motivated by this, we formulate a novel fair recommendation framework, called Problem (FAIR), that not only maximizes the platform's revenue, but also accommodates varying fairness considerations from the perspectives of items and users. Our framework's distinguishing trait lies in its flexibility -- it allows the platform to specify any definitions of item/user fairness that are deemed appropriate, as well as decide the "price of fairness" it is willing to pay to ensure fairness for other stakeholders. We further examine Problem (FAIR) in a dynamic online setting, where the platform needs to learn user data and generate fair recommendations simultaneously in real time, which are two tasks that are often at odds. In face of this additional challenge, we devise a low-regret online recommendation algorithm, called FORM, that effectively balances the act of learning and performing fair recommendation. Our theoretical analysis confirms that FORM proficiently maintains the platform's revenue, while ensuring desired levels of fairness for both items and users. Finally, we demonstrate the efficacy of our framework and method via several case studies on real-world data.

翻译：当今在线平台高度依赖算法推荐来提升用户参与度和驱动收入增长。然而，此类算法推荐可能影响平台、项目（卖家）和用户（顾客）等不同利益相关者，各方均具有独特目标。在这种多边平台中，寻找恰当的平衡点成为复杂的运营挑战。受此启发，我们构建了一个名为FAIR问题的新型公平推荐框架，该框架不仅最大化平台收益，还从项目和用户角度兼顾不同公平性考量。该框架的显著特征在于其灵活性——允许平台指定任何认为合适的项目/用户公平性定义，并决定为保障其他利益相关者公平性而愿意承担的"公平代价"。我们进一步在动态在线环境中研究FAIR问题，该场景要求平台同时实时学习用户数据并生成公平推荐，这两项任务往往相互矛盾。面对这一额外挑战，我们设计了名为FORM的低遗憾在线推荐算法，该算法能有效平衡学习行为与公平推荐执行。理论分析证实，FORM在确保项目和用户达到预期公平水平的同时，能熟练维持平台收益。最后，我们通过多个真实数据案例研究验证了所提框架与方法的有效性。