Unsupervised Learning to Rank Aggregation using Parameterized Function Optimization

This paper proposes a novel unsupervised rank aggregation method using parameterized function optimization (PFO). This algorithm derives a parameterized rank aggregation model by minimizing the energy of weighted standard deviations of rank lists associated with different rankers or attributes. Parameters, in this problem, are weights representing the impact of rank lists on the final aggregated rank. The proposed learning to rank aggregation method is efficient (linear time complexity) and its accuracy compares favorably with pairwise preference methods (with polynomial time complexity). Two rounds of experiments are run to show the success of PFO in rank aggregation: one on the learning to rank (LETOR) benchmark dataset to show its success in unsupervised rank aggregation and the other on three university ranking datasets to solve a practical problem in education. The experimental results on the LETOR show that PFO significantly outperforms the baseline results and show promising performances in comparison with recent high performance methods developed for unsupervised rank aggregation. The university ranks obtained by our model compare favorably with the ranks reported by well-known organizations. Success of the PFO model for performing unsupervised rank aggregation, specifically on practical problems, supports the use of the algorithm in difficult ranking scenarios without ground truth.