Fast learning of document ranking functions with the committee perceptron

This paper presents a new variant of the perceptron algo-rithm using selective committee averaging (or voting). We apply this agorithm to the problem of learning ranking func-tions for document retrieval, known as the “Learning to Rank ” problem. Most previous algorithms proposed to ad-dress this problem focus on minimizing the number of mis-ranked document pairs in the training set. The commit-tee perceptron algorithm improves upon existing solutions by biasing the final solution towards maximizing an arbi-trary rank-based performance metrics. This method per-forms comparably or better than two state-of-the-art rank learning algorithms, and also provides significant training time improvements over those methods, showing over a 45-fold reduction in training time compared to ranking SVM