End-to-end Learning for Fair Ranking Systems
This addresses fairness guarantees in ranking systems for users and item groups, representing a novel advancement beyond incremental improvements.
The paper tackles the problem of ensuring fairness in learning-to-rank systems by introducing SPOFR, an end-to-end framework that guarantees fairness constraints while optimizing utility, and it significantly improves over state-of-the-art methods on established metrics.
The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some notion of fairness among specified item groups. While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the proposed ranking policies. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff. SPOFR is shown to significantly improve current state-of-the-art fair learning-to-rank systems with respect to established performance metrics.