Workload-driven learning of mallows mixtures with pairwise preference data
Julia Stoyanovich, Lovro Ilijašić, Haoyue Ping · 2016
In this paper we present a framework for learning mixtures of Mallows models from large samples of incomplete preferences. The problem we address is of significant practical importance in social choice, recommender systems, and other domains where it is required to aggregate, or otherwise analyze, preferences of a heterogeneous user base.