Generalized Method-of-Moments for Rank Aggregation
Abstract
In this paper we propose a class of efficient Generalized Method-of-Moments(GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for the output of GMM to be unique, and identify a general class of consistent and inconsistent breakings. We then show by theory and experiments that our algorithms run significantly faster than the classical Minorize-Maximization (MM) algorithm, while achieving competitive statistical efficiency.
Cite
Text
Soufiani et al. "Generalized Method-of-Moments for Rank Aggregation." Neural Information Processing Systems, 2013.Markdown
[Soufiani et al. "Generalized Method-of-Moments for Rank Aggregation." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/soufiani2013neurips-generalized-a/)BibTeX
@inproceedings{soufiani2013neurips-generalized-a,
title = {{Generalized Method-of-Moments for Rank Aggregation}},
author = {Soufiani, Hossein Azari and Chen, William and Parkes, David C. and Xia, Lirong},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {2706-2714},
url = {https://mlanthology.org/neurips/2013/soufiani2013neurips-generalized-a/}
}