Learning to rankwith bregman divergences and monotone retargeting

Sreangsu Acharyya, Oluwasanmi Koyejo, Joydeep Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper introduces a novel approach for learning to rank (LETOR) based on the notion of monotone retargeting. It involves minimizing a divergence between all monotonic increasing transformations of the training scores and a parameterized prediction function. The minimization is both over the transformations as well as over the parameters. It is applied to Bregman divergences, a large class of distance like functions that were recently shown to be the unique class that is statistically consistent with the normalized discounted gain (NDCG) criterion [19]. The algorithm uses alternating projection style updates, in which one set of simultaneous projections can be computed independent of the Bregman divergence and the other reduces to parameter estimation of a generalized linear model. This results in easily implemented, efficiently parallelizable algorithm for the LETOR task that enjoys global optimum guarantees under mild conditions. We present empirical results on benchmark datasets showing that this approach can outperform the state of the art NDCG consistent techniques.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
Pages16-25
Number of pages10
StatePublished - Dec 1 2012
Externally publishedYes
Event28th Conference on Uncertainty in Artificial Intelligence, UAI 2012 - Catalina Island, CA, United States
Duration: Aug 15 2012Aug 17 2012

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

Other

Other28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
CountryUnited States
CityCatalina Island, CA
Period8/15/128/17/12

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Acharyya, S., Koyejo, O., & Ghosh, J. (2012). Learning to rankwith bregman divergences and monotone retargeting. In Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012 (pp. 16-25). (Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012).