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Ranking and Contextual Selection

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  • Gregory Keslin

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208)

  • Barry L. Nelson

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208)

  • Bernardo Pagnoncelli

    (SKEMA Business School, Université Côte d’Azur, 59777 Lille, France)

  • Matthew Plumlee

    (Amazon, Seattle, Washington 98109)

  • Hamed Rahimian

    (Department of Industrial Engineering, Clemson University, Clemson, South Carolina 29634)

Abstract

This paper proposes a new ranking-and-selection procedure, called ranking and contextual selection, in which covariates provide context for data-driven decisions. Our procedure optimizes over a set of covariate design points off-line and then, given an actual observation of the covariate, makes an online decision based on classification—a distinctly new approach. We prove the existence of an experimental design that yields a pointwise probability of good selection guarantee and derive a postexperiment assessment of our procedure that provides an optimality gap upper bound with guaranteed coverage for decisions with respect to future covariates. We illustrate ranking and contextual selection with an application to assortment optimization using data available from Yahoo!.

Suggested Citation

  • Gregory Keslin & Barry L. Nelson & Bernardo Pagnoncelli & Matthew Plumlee & Hamed Rahimian, 2025. "Ranking and Contextual Selection," Operations Research, INFORMS, vol. 73(5), pages 2695-2707, September.
    • Handle: RePEc:inm:oropre:v:73:y:2025:i:5:p:2695-2707
    DOI: 10.1287/opre.2023.0378
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