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Pairwise learning to rank methods such as RankSVM give good performance, but suffer from the computational burden of optimizing an objective defined over O(n 2) possible pairs for data sets with n examples. In this paper, the authors remove this super-linear dependence on training set size by sampling pairs from an implicit pairwise expansion and applying efficient stochastic gradient descent learners for approximate SVMs. Results show orders-of-magnitude reduction in training time with no observable loss in ranking performance.
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