Large-scale Linear RankSVM

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following the recent development of linear SVM for classification, linear rankSVM may give competitive performance for large and sparse data. Many existing works have studied linear rankSVM. Their focus is on the computational efficiency when the number of preference pairs is large. In this paper, we systematically study past works, discuss their advantages/disad-vantages, and propose an efficient algorithm. Different implementation issues and extensions are discussed with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.