Preference Learning

This paper considers rank and preference modeling for the case in which data arrive sequentially, rather than in a batch. The goal is to compute the posterior distribution incrementally in time, so that it can be quickly updated when new data arrives. To this end, we develop a sequential Monte Carlo algorithm for the Bayesian Mallows model. arXiv preprint currently under revision.

This paper studies the analysis of rank and preference data. We consider both complete rankings, partial rankings, and pairwise preferences. We develop a complete Bayesian framework for estimating Mallows’ rank model in all this cases, including clustering of users with similar preferences and preference prediction. Published in Journal of Machine Learning Research. Joint first authorship with Valeria Vitelli.