Large-scale Kernel RankSVM
Tzu-Ming Kuo, Ching-pei Lee, Chih‐Jen Lin · 2014
Learning to rank is an important task for recommendation systems, online advertisement and web search. Among those learning to rank methods, rankSVM is a widely used model. Both linear and nonlinear (kernel) rankSVM have been extensively studied, but the lengthy training time of kernel rankSVM remains a challenging issue. In this paper, after discussing difficulties of training kernel rankSVM, we propose an efficient method to handle these problems. The idea is to reduce the number of variables from quadratic to linear with respect to the number of training instances, and efficiently evaluate the pairwise losses. Our setting is applicable to a variety of loss functions. Further, general optimization methods can be easily applied to solve the reformulated problem. Implementation issues are also carefully considered. Experiments show that our method is faster than state-of-the-art methods for training kernel rankSVM.