Maximizing Marginal Fairness for Dynamic Learning to Rank

TL;DR

This paper introduces Maximal Marginal Fairness (MMF), a new dynamic ranking method that balances relevance and fairness in top-k results, improving efficiency and effectiveness over existing algorithms.

Contribution

The paper proposes MMF, a novel algorithm that optimally balances relevance and fairness in dynamic rankings with theoretical and empirical validation.

Findings

MMF outperforms state-of-the-art algorithms in relevance and fairness.

Achieves better top-k ranking fairness with minimal long list fairness compromise.

Demonstrates superior efficiency and effectiveness through analysis and experiments.

Abstract

Rankings, especially those in search and recommendation systems, often determine how people access information and how information is exposed to people. Therefore, how to balance the relevance and fairness of information exposure is considered as one of the key problems for modern IR systems. As conventional ranking frameworks that myopically sorts documents with their relevance will inevitably introduce unfair result exposure, recent studies on ranking fairness mostly focus on dynamic ranking paradigms where result rankings can be adapted in real-time to support fairness in groups (i.e., races, genders, etc.). Existing studies on fairness in dynamic learning to rank, however, often achieve the overall fairness of document exposure in ranked lists by significantly sacrificing the performance of result relevance and fairness on the top results. To address this problem, we propose a fair and unbiased ranking method named Maximal Marginal Fairness (MMF). The algorithm integrates unbiased estimators for both relevance and merit-based fairness while providing an explicit controller that balances the selection of documents to maximize the marginal relevance and fairness in top-k results. Theoretical and empirical analysis shows that, with small compromises on long list fairness, our method achieves superior efficiency and effectiveness comparing to the state-of-the-art algorithms in both relevance and fairness for top-k rankings.