In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing 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 prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to clustering the preferences for university programmes of Irish secondary school graduates.
['Francois Caron', 'Yee Whye Teh', 'Thomas Brendan Murphy']
Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data