Computing Parametric Ranking Models via Rank-Breaking

Hossein Azari Soufiani, David Parkes, Lirong Xia
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):360-368, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-soufiani14, title = {Computing Parametric Ranking Models via Rank-Breaking}, author = {Soufiani, Hossein Azari and Parkes, David and Xia, Lirong}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {360--368}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/soufiani14.pdf}, url = {https://proceedings.mlr.press/v32/soufiani14.html}, 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.} }
Endnote
%0 Conference Paper %T Computing Parametric Ranking Models via Rank-Breaking %A Hossein Azari Soufiani %A David Parkes %A Lirong Xia %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-soufiani14 %I PMLR %P 360--368 %U https://proceedings.mlr.press/v32/soufiani14.html %V 32 %N 1 %X 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.
RIS
TY - CPAPER TI - Computing Parametric Ranking Models via Rank-Breaking AU - Hossein Azari Soufiani AU - David Parkes AU - Lirong Xia BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-soufiani14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 1 SP - 360 EP - 368 L1 - http://proceedings.mlr.press/v32/soufiani14.pdf UR - https://proceedings.mlr.press/v32/soufiani14.html AB - 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. ER -
APA
Soufiani, H.A., Parkes, D. & Xia, L.. (2014). Computing Parametric Ranking Models via Rank-Breaking. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(1):360-368 Available from https://proceedings.mlr.press/v32/soufiani14.html.

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