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

• URL: http://arxiv.org/abs/2602.00495v1
• Date: Sat, 31 Jan 2026 03:44:31 GMT
• Title: Equity vs. Equality: Optimizing Ranking Fairness for Tailored Provider Needs
• Authors: Yiteng Tu, Weihang Su, Shuguang Han, Yiqun Liu, Qingyao Ai,
• Abstract summary: We introduce an equity-oriented fairness framework that explicitly models each provider's preferences over key outcomes such as exposure and sales.<n>We develop EquityRank, a gradient-based algorithm that jointly optimize user-side effectiveness and provider-side equity.
• Score: 29.32978829799322
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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