Bayesian nonparametric models for ranked data
Machine Learning
2012-11-20 v1 Machine Learning
Methodology
Abstract
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs sampler for posterior simulation. We develop a time-varying extension of our model, and apply it to the New York Times lists of weekly bestselling books.
Cite
@article{arxiv.1211.4321,
title = {Bayesian nonparametric models for ranked data},
author = {Francois Caron and Yee Whye Teh},
journal= {arXiv preprint arXiv:1211.4321},
year = {2012}
}
Comments
NIPS - Neural Information Processing Systems (2012)