Bayesian nonparametric models for ranked data

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

Authors

Francois Caron, Yee Teh

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.