Computing Parametric Ranking Models via Rank-Breaking

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Computing Parametric Ranking Models via Rank-Breaking

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Title: Computing Parametric Ranking Models via Rank-Breaking
Author: Soufiani, Hossein Azari; Parkes, David C.; Xia, Lirong

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Citation: Soufiani, Hossein Azari, David Parkes, and Lirong Xia. 2014. Computing Parametric Ranking Models via Rank-Breaking. In Proceedings of the 31st International Conference on Machine Learning (ICML14), Beijing, China, June 22-24, 2014: 360–368.
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Abstract: Rank breaking is a methodology introduced by Azari Soufiani et al. (2013a) for applying a Generalized Method of Moments (GMM) algorithm to the estimation of parametric ranking models. Breaking takes full rankings and breaks, or splits them up, into counts for pairs of alternatives that occur in particular positions (e.g., first place and second place, second place and third place). GMMs are of interest because they can achieve significant speed-up relative to maximum likelihood approaches and comparable statistical efficiency. We characterize the breakings for which the estimator is consistent for random utility models (RUMs) including Plackett-Luce and Normal-RUM, develop a general sufficient condition for a full breaking to be the only consistent breaking, and provide a trichotomy theorem in regard to single-edge breakings. Experimental results are presented to show the computational efficiency along with statistical performance of the proposed method.
Published Version: http://jmlr.org/proceedings/papers/v32/soufiani14.html
Other Sources: http://www.cs.rpi.edu/~xial/Files/GMMRUM.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17367906
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