End-to-End Learning for Fair Ranking Systems

James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu

Research output: Chapter in Book/Entry/PoemConference contribution

4 Scopus citations


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 predicted rankings. 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 on current state-of-the-art fair learning-to-rank systems with respect to established performance metrics.

Original languageEnglish (US)
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Number of pages11
ISBN (Electronic)9781450390965
StatePublished - Apr 25 2022
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: Apr 25 2022Apr 29 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022


Conference31st ACM World Wide Web Conference, WWW 2022
CityVirtual, Online


  • Decision Focused Learning
  • Fairness
  • Learning to Rank
  • Optimization
  • Smart Predict and Optimize

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software


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