Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions

2026-02-03

AI Summary

This work addresses a critical limitation in existing ranking fairness approaches, which predominantly rely on exposure position while neglecting contextual factors such as time that significantly influence content providers’ actual revenue. To bridge this gap, the paper introduces the novel concept of “revenue fairness,” explicitly modeling the nonlinear relationship between time and income. It proposes a Dynamic Income Derivative-aware Ranking Framework (DIDRF) that dynamically adjusts ranking strategies based on marginal revenue gradients. By integrating Taylor expansion approximations within an online-offline joint optimization framework, DIDRF effectively captures temporal dynamics in revenue generation. Extensive experiments demonstrate that DIDRF consistently outperforms state-of-the-art methods across diverse time-revenue functions, achieving substantial improvements in both revenue fairness and overall ranking performance in both offline and online evaluations.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Ranking is central to information distribution in web search and recommendation. Nowadays, in ranking optimization, the fairness to item providers is viewed as a crucial factor alongside ranking relevance for users. There are currently numerous concepts of fairness and one widely recognized fairness concept is Exposure Fairness. However, it relies primarily on exposure determined solely by position, overlooking other factors that significantly influence income, such as time. To address this limitation, we propose to study ranking fairness when the provider utility is influenced by other contextual factors and is neither equal to nor proportional to item exposure. We give a formal definition of Income Fairness and develop a corresponding measurement metric. Simulated experiments show that existing-exposure-fairness-based ranking algorithms fail to optimize the proposed income fairness. Therefore, we propose the Dynamic-Income-Derivative-aware Ranking Fairness algorithm, which, based on the marginal income gain at the present timestep, uses Taylor-expansion-based gradients to simultaneously optimize effectiveness and income fairness. In both offline and online settings with diverse time-income functions, DIDRF consistently outperforms state-of-the-art methods.

Problem

• ranking fairness
• exposure fairness
• income fairness
• time-income functions
• provider utility

Innovation

• Income Fairness
• Non-linear Time-Income Functions
• Dynamic-Income-Derivative-aware Ranking
• Taylor-expansion-based Gradients
• Exposure Fairness