Generalized method of moments algorithm for learning mixtures of Plackett-Luce models
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Authors
Piech, Peter D.
Issue Date
2016-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
Abstract
It is often the primary interest of certain voting systems to be able to generate an aggregate ranking over a set of candidates or alternatives from the preferences of individual agents or voters. The Plackett-Luce model is one of the most studied models for statistically describing discrete choice ordinal preferences and summarizing rank data in the machine learning subarea of rank aggregation which has also been widely researched from the approach of problems in computational social choice. Much work has been done by the machine learning community in developing algorithms to efficiently estimate Plackett-Luce model parameters with wide-ranging real-world applications of rank data in e-commerce and political science, such as meta-search engines, consumer products rankings, and presidential elections. In machine learning tasks, a mixture of models can sometimes better fit the data more closely than a single model alone, and so naturally, mixtures of Plackett-Luce models can also confer the same benefits for rank data.
Description
May 2016
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY