Ranking plays a central role in connecting users and providers in Information Retrieval (IR) systems, making provider-side fairness an important challenge. While recent research has begun to address fairness in ranking, most existing approaches adopt an equality-based perspective, aiming to ensure that providers with similar content receive similar exposure. However, it overlooks the diverse needs of real-world providers, whose utility from ranking may depend not only on exposure but also on outcomes like sales or engagement. Consequently, exposure-based fairness may not accurately capture the true utility perceived by different providers with varying priorities. To this end, we introduce an equity-oriented fairness framework that explicitly models each provider's preferences over key outcomes such as exposure and sales, thus evaluating whether a ranking algorithm can fulfill these individualized goals while maintaining overall fairness across providers. Based on this framework, we develop EquityRank, a gradient-based algorithm that jointly optimizes user-side effectiveness and provider-side equity. Extensive offline and online simulations demonstrate that EquityRank offers improved trade-offs between effectiveness and fairness and adapts to heterogeneous provider needs.

翻译：在信息检索（IR）系统中，排序在连接用户与提供者方面扮演着核心角色，这使得提供者侧公平性成为一个重要挑战。尽管近期研究已开始关注排序中的公平性问题，但现有方法大多基于平等视角，旨在确保内容相似的提供者获得相似的曝光度。然而，这种方法忽视了现实世界中提供者需求的多样性——提供者从排序中获得的效用不仅取决于曝光度，还可能取决于销售额或参与度等结果指标。因此，基于曝光度的公平性可能无法准确反映具有不同优先级的提供者所感知的真实效用。为此，我们提出一个以公平为导向的公平性框架，该框架显式建模每个提供者对关键结果（如曝光度与销售额）的偏好，从而评估排序算法在维持提供者间整体公平性的同时，能否实现这些个性化目标。基于此框架，我们开发了EquityRank——一种基于梯度的算法，可联合优化用户侧有效性与提供者侧公平性。大量离线与在线模拟实验表明，EquityRank在有效性与公平性之间实现了更优的权衡，并能适应异构化的提供者需求。