Modeling Intransitivity In Matchup And Comparison Data

We present a method for learning potentially intransitive preference relations from pairwise comparison and matchup data. Unlike standard preference-learning models that represent the properties of each item/player as a single number, our method infers a multi-dimensional representation for the different aspects of each item/player's strength. We show that our model can represent any pairwise stochastic preference relation and provide a comprehensive evaluation of its predictive performance on a wide range of pairwise comparison tasks and matchup problems from online video games and sports, to peer grading and election. We find that several of these task especially matchups in online video games show substantial intransitivity that our method can model effectively.