Generalized Method-of-Moments for Rank Aggregation

نویسندگان

  • Hossein Azari Soufiani
  • William Z. Chen
  • David C. Parkes
  • Lirong Xia
چکیده

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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تاریخ انتشار 2013