Bayesian Aggregation of Order-Based Rank Data

Abstract

Rank aggregation, that is, combining several ranking functions (called base rankers) to get aggregated, usually stronger rankings of a given set of items, is encountered in many disciplines. Most methods in the literature assume that base rankers of interest are equally reliable. It is very common in practice, however, that some rankers are more informative and reliable than others. It is desirable to distinguish high quality base rankers from low quality ones and treat them differently. Some methods achieve this by assigning prespecified weights to base rankers. But there are no systematic and principled strategies for designing a proper weighting scheme for a practical problem. In this article, we propose a Bayesian approach, called Bayesian aggregation of rank data (BARD), to overcome this limitation. By attaching a quality parameter to each base ranker and estimating these parameters along with the aggregation process, BARD measures reliabilities of base rankers in a quantitative way and makes use of this information to improve the aggregated ranking. In addition, we design a method to detect highly correlated rankers and to account for their information redundancy appropriately. Both simulation studies and real data applications show that BARD significantly outperforms existing methods when equality of base rankers varies greatly.