Vertical Allocation-based Fair Exposure Amortizing in Ranking

TL;DR

This paper introduces VerFair, a novel algorithm for ranking that improves exposure fairness while maintaining relevance, addressing limitations of existing methods by leveraging consumer prior knowledge.

Contribution

The paper proposes VerFair, a new algorithm that enhances fairness-relevance tradeoff in ranking by utilizing consumer prior knowledge, outperforming existing methods.

Findings

VerFair achieves better fairness-performance balance in experiments.

It outperforms state-of-the-art fair ranking algorithms.

Extensive tests on real datasets validate its effectiveness.

Abstract

Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by ranking items only according to relevance will lead to unfair distribution of exposure for items, followed by unfair opportunities and economic gains for item producers/providers. Such unfairness will force providers to leave the system and discourage new providers from coming in. Eventually, fewer purchase options would be left for consumers, and the utilities of both consumers and providers would be harmed. Thus, to maintain a balance between ranking relevance and fairness is crucial for both parties. In this paper, we focus on the exposure fairness in ranking services. We demonstrate that existing methods for amortized fairness optimization could be suboptimal in terms of fairness-relevance tradeoff because they fail to utilize the prior knowledge of consumers. We further propose a novel algorithm named Vertical Allocation-based Fair Exposure Amortizing in Ranking, or VerFair, to reach a better balance between exposure fairness and ranking performance. Extensive experiments on three real-world datasets show that VerFair significantly outperforms state-of-the-art fair ranking algorithms in fairness-performance trade-offs from both the individual level and the group level.