Publication: Generalized Method-of-Moments for Rank Aggregation
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Date
2013
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Neural Information Processing Systems Foundation, Inc.
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Azari Soufiani, Hossein, William Z. Chen, David C. Parkes, and Lirong Xia. 2013. Generalized Method-of-Moments for Rank Aggregation. Presented at NIPS 2013, Lake Tahoe, Nevada, Dec 5-8, 2013. In Advances in Neural Information Processing Systems 26, ed. C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, 2706-2714. La Jolla, CA: Neural Information Processing Systems Foundation, Inc.
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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.
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