X-DART: Blending Dropout and Pruning for Efficient Learning to Rank

Abstract

In this paper we propose X-DART, a new Learning to Rank algorithm focusing on the training of robust and compact ranking models. Motivated from the observation that the last trees of MART models impact the prediction of only a few instances of the training set, we borrow from the DART algorithm the dropout strategy consisting in temporarily dropping some of the trees from the ensemble while new weak learners are trained. However, differently from this algorithm we drop permanently these trees on the basis of smart choices driven by accuracy measured on the validation set. Experiments conducted on publicly available datasets shows that X-DART outperforms DART in training models providing the same effectiveness by employing up to 40% less trees.