Ranking systems in web search and recommendation allocate attention among items and providers, and therefore need to balance relevance-based effectiveness with provider fairness. Existing fair-ranking methods commonly focus on exposure fairness, where cumulative exposure is allocated in proportion to item merit. However, exposure is often only an intermediate signal: the actual utility received by a provider may depend on context-dependent conversion from exposure to income, such as clicks, purchases, or advertising value. This paper studies fair ranking under context-dependent provider utility, which we refer to as income. We formalize income fairness by requiring cumulative provider income to be proportional to relevance, and define an income-unfairness metric based on this proportionality condition. We then propose DIDRF, a Dynamic-Income-Derivative-aware Ranking Fairness algorithm for income-fair ranking. DIDRF uses the quadratic structure of income-fairness violations to derive a state-aware scoring rule that jointly considers ranking effectiveness and the marginal effect of each ranking decision on cumulative income fairness. Experiments on standard learning-to-rank datasets with log-calibrated semi-synthetic income environments based on advertising and e-commerce logs show that DIDRF consistently improves income fairness over representative fair-ranking baselines while preserving competitive ranking effectiveness.

翻译：在网页搜索和推荐系统中，排序算法在项目与提供者之间分配注意力，因此需在基于相关性的有效性评估与提供者公平性之间取得平衡。现有公平排序方法通常聚焦于曝光公平性——要求累计曝光量按项目价值成比例分配。然而，曝光往往仅是中间信号：提供者实际获得的效用取决于从曝光到收入（如点击、购买或广告价值）的上下文依赖性转化。本文研究上下文依赖的提供者效用（本文称之为“收入”）下的公平排序问题。我们通过要求提供者累计收入与相关性成比例来形式化收入公平性，并基于该比例条件定义收入不公平度指标。进而提出DIDRF（动态收入导数感知排序公平性算法），该算法利用收入公平性违规的二次结构，推导出状态感知评分规则，同时考虑排序有效性与每个排序决策对累计收入公平性的边际效应。在基于广告与电商日志的对数校准半合成收入环境下，基于标准学习排序数据集进行的实验表明：DIDRF在保持竞争力排序有效性的同时，相较于代表性公平排序基准方法显著提升了收入公平性。