Dirichlet Process Mixtures of Generalized Mallows Models

TL;DR

This paper introduces a Dirichlet process mixture model for discrete incomplete rankings, employing two Gibbs sampling methods to improve clustering inference and demonstrate effectiveness on real-world ranking data.

Contribution

It proposes a novel Dirichlet process mixture model for ranking data and compares two Gibbs sampling inference techniques, highlighting their advantages.

Findings

Approximation improves convergence in Gibbs sampling

Dirichlet process model outperforms alternative clustering methods

Method effectively explores large real-world ranking datasets

Abstract

We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large realworld ranking datasets.