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. Keywords Matchup, Pairwise Comparison, Representation Learning, Ranking, Sports, Games

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Shuo Chen, Thorsten Joachims
• Keywords: Data Mining, WSDM 2016