Azari Soufiani, HosseinChen, WilliamParkes, DavidXia, Lirong2014-07-252013Azari 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.9781632660244http://nrs.harvard.edu/urn-3:HUL.InstRepos:12561350In 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.en-USConference Paper2014-07-25