Direct optimization of ranking measures for learning to rank models

We present a novel learning algorithm, DirectRank, which directly and exactly optimizes ranking measures without resorting to any upper bounds or approximations. Our approach is essentially an iterative coordinate ascent method. In each iteration, we choose one coordinate and only update the corresponding parameter, with all others remaining fixed. Since the ranking measure is a stepwise function of a single parameter, we propose a novel line search algorithm that can locate the interval with the best ranking measure along this coordinate quite efficiently. In order to stabilize our system in small datasets, we construct a probabilistic framework for document-query pairs to maximize the likelihood of the objective permutation of top-$\tau$ documents. This iterative procedure ensures convergence. Furthermore, we integrate regression trees as our weak learners in order to consider the correlation between the different features. Experiments on LETOR datasets and two large datasets, Yahoo challenge data and Microsoft 30K web data, show an improvement over state-of-the-art systems.