Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions
For providers in web search and recommendation, this work addresses the gap between exposure fairness and actual utility fairness, offering a method to optimize for income fairness.
This paper addresses the problem of provider fairness in ranking systems, where the actual utility (income) from exposure depends on context. They propose DIDRF, an algorithm that optimizes for income fairness, and show it improves income fairness over baselines while maintaining competitive effectiveness on semi-synthetic datasets.
Ranking systems in web search and recommendation allocate attention among items and providers, and therefore need to balance relevance-based effectiveness with provider fairness. Existing fair-ranking methods commonly focus on exposure fairness, where cumulative exposure is allocated in proportion to item merit. However, exposure is often only an intermediate signal: the actual utility received by a provider may depend on context-dependent conversion from exposure to income, such as clicks, purchases, or advertising value. This paper studies fair ranking under context-dependent provider utility, which we refer to as income. We formalize income fairness by requiring cumulative provider income to be proportional to relevance, and define an income-unfairness metric based on this proportionality condition. We then propose DIDRF, a Dynamic-Income-Derivative-aware Ranking Fairness algorithm for income-fair ranking. DIDRF uses the quadratic structure of income-fairness violations to derive a state-aware scoring rule that jointly considers ranking effectiveness and the marginal effect of each ranking decision on cumulative income fairness. Experiments on standard learning-to-rank datasets with log-calibrated semi-synthetic income environments based on advertising and e-commerce logs show that DIDRF consistently improves income fairness over representative fair-ranking baselines while preserving competitive ranking effectiveness.