%0 Journal Article
%J Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
%D 2007
%T A fast algorithm for learning large scale preference relations
%A Raykar,V.C.
%A Duraiswami, Ramani
%A Krishnapuram,B.
%X We consider the problem of learning the rank-ing function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on training data. Relying on an ϵ-exact approx- imation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learn- ing ranking functions from O(m2), to O(m), where m is the size of the training data. Experiments on public benchmarks for ordi- nal regression and collaborative filtering show that the proposed algorithm is as accurate as the best available methods in terms of rank- ing accuracy, when trained on the same data, and is several orders of magnitude faster.
%B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
%V 2
%P 388 - 395
%8 2007///
%G eng