  Web page ranking and collaborative filtering require the optimization of
sophisticated performance measures. Current Support Vector approaches are
unable to optimize them directly and focus on pairwise comparisons instead. We
present a new approach which allows direct optimization of the relevant loss
functions. This is achieved via structured estimation in Hilbert spaces. It is
most related to Max-Margin-Markov networks optimization of multivariate
performance measures. Key to our approach is that during training the ranking
problem can be viewed as a linear assignment problem, which can be solved by
the Hungarian Marriage algorithm. At test time, a sort operation is sufficient,
as our algorithm assigns a relevance score to every (document, query) pair.
Experiments show that the our algorithm is fast and that it works very well.
