Ranking is central to information distribution in web search and recommendation. Nowadays, in ranking optimization, the fairness to item providers is viewed as a crucial factor alongside ranking relevance for users. There are currently numerous concepts of fairness and one widely recognized fairness concept is Exposure Fairness. However, it relies primarily on exposure determined solely by position, overlooking other factors that significantly influence income, such as time. To address this limitation, we propose to study ranking fairness when the provider utility is influenced by other contextual factors and is neither equal to nor proportional to item exposure. We give a formal definition of Income Fairness and develop a corresponding measurement metric. Simulated experiments show that existing-exposure-fairness-based ranking algorithms fail to optimize the proposed income fairness. Therefore, we propose the Dynamic-Income-Derivative-aware Ranking Fairness algorithm, which, based on the marginal income gain at the present timestep, uses Taylor-expansion-based gradients to simultaneously optimize effectiveness and income fairness. In both offline and online settings with diverse time-income functions, DIDRF consistently outperforms state-of-the-art methods.

翻译：排序在网页搜索与推荐的信息分发中处于核心地位。当前，排序优化中，项目提供方的公平性与面向用户的排序相关性被视为同等关键的因素。现有公平性概念众多，其中"曝光公平性"被广泛认可。然而，该概念主要依赖于仅由排序位置决定的曝光度，忽视了其他显著影响收益的因素（例如时间）。为克服此局限，我们提出研究当提供方效用受其他上下文因素影响、且既不等于也不与项目曝光度成比例时的排序公平性问题。我们给出了"收益公平性"的形式化定义，并构建了相应的度量指标。仿真实验表明，现有基于曝光公平性的排序算法无法优化所提出的收益公平性。因此，我们提出了动态收益导数感知排序公平性算法，该算法基于当前时间步的边际收益增益，利用泰勒展开式梯度同时优化排序效果与收益公平性。在具有多样化时间-收益函数的离线与在线场景中，DIDRF算法均持续优于现有最优方法。