Bayesian Nonparametric Models for Ranked Data
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 then develop a time-varying extension of our model, and apply our model to the New York Times lists of weekly bestselling books.
Cite
Text
Caron and Teh. "Bayesian Nonparametric Models for Ranked Data." Neural Information Processing Systems, 2012.Markdown
[Caron and Teh. "Bayesian Nonparametric Models for Ranked Data." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/caron2012neurips-bayesian-a/)BibTeX
@inproceedings{caron2012neurips-bayesian-a,
title = {{Bayesian Nonparametric Models for Ranked Data}},
author = {Caron, Francois and Teh, Yee W.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {1520-1528},
url = {https://mlanthology.org/neurips/2012/caron2012neurips-bayesian-a/}
}