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arXiv · 2602.00495

Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs

Yiteng Tu·Weihang Su·Shuguang Han·Yiqun Liu·Qingyao Ai
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Abstract

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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Yiteng Tu, Weihang Su, Shuguang Han, Yiqun Liu, Qingyao Ai. 2026-01-31. Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs. https://arxiv.org/abs/2602.00495

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