Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data

Abstract

Accelerated Algorithms for RankingAssigning ranking scores to items based on observed comparison data (e.g., paired comparisons, choice, and full ranking outcomes) has been of continued interest in a wide range of applications, including information search, aggregation of social opinions, electronic commerce, online gaming platforms, and more recently, evaluation of machine learning algorithms. The key problem is to compute ranking scores, which are of interest for quantifying the strength of skills, relevancies, or preferences, and prediction of ranking outcomes. One of the most popular statistical models of ranking outcomes is the Bradley–Terry model for paired comparisons and its extensions to choice and full ranking outcomes. In “Accelerated MM Algorithms for Inference of Ranking Scores from Comparison Data,” M. Vojnovic, S.-Y. Yun, and K. Zhou show that a popular MM algorithm for inference of ranking scores for generalized Bradley–Terry ranking models suffers a slow convergence issue, and they propose a new accelerated algorithm that resolves this shortcoming and can yield substantial convergence speedups.