提出按需公平的排序框架，让不同商家获得更符合自身目标的曝光。
Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs
- 基于商家对曝光与销量等目标的偏好，构建个性化公平评估体系
- 提出EquityRank算法，在用户效果与商家公平间取得更好平衡
- 适合关注电商、内容平台等个性化推荐场景的研究者与从业者
排序在信息检索系统中连接用户与服务提供方，因此提供方层面的公平性成为重要挑战。现有研究多从均等暴露出发，但忽略了真实场景中不同提供方对曝光、销量或互动等结果的差异化需求。为此，本文提出一种以公平性为导向的框架，显式建模每个提供方对曝光、销售等关键结果的偏好，评估排序算法是否满足其个性化目标的同时保持整体公平。基于此框架，我们开发了梯度优化的EquityRank算法，联合优化用户侧有效性与提供方侧公平性。大量离线与在线模拟表明，EquityRank在有效性和公平性之间实现了更优权衡，并能适应异构的提供方需求。
原文摘要 · Abstract (English)
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.
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