Optimization of Ranking Measures

Web page ranking requires the optimization of sophisticated performance measures. Current approaches only minimize measures indirectly related to performance scores. We present a new approach which allows optimization of an upper bound of the appropriate loss function. This is achieved via structured estimation, where in our case the input corresponds to a set of documents and the output is a ranking. Training is efficient since computing the loss function can be done via a linear assignment problem. At test time, a sorting operation suffices, as our algorithm assigns a relevance score to every (document, query) pair. Moreover, we provide a general method for finding tighter nonconvex relaxations of structured loss functions. Experiments show that the our algorithm yields improved accuracies on several public and commercial ranking datasets.