A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets

TitleA Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
Publication TypeJournal Articles
Year of Publication2008
AuthorsRaykar VC, Duraiswami R, Krishnapuram B
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination1158 - 1170
Date Published2008/07//
ISBN Number0162-8828
KeywordsAutomated;, Factual;Information Storage and Retrieval;Likelihood Functions;Models, Statistical;Pattern Recognition, Wilcoxon-Mann-Whitney statistics;collaborative filtering;error function;gradient algorithm;large-scale data sets;learning ranking functions;ranking function;training data;computational complexity;error analysis;learning (artificial intelligence);regressio

We consider the problem of learning a ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an e-accurate approximation for the error function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from O(m2) to O(m), where m is the number of training samples. Experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy, when the algorithms are trained on the same data. However, since it is several orders of magnitude faster than the current state-of-the-art approaches, it is able to leverage much larger training data sets.