X-DART: Blending Dropouts and Pruning for Eﬃcient Learning To Rank
Accepted at SIGIR ’17: ACM Conference on Research and Development in Information Retrieval .
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 drops 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.
 Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raﬀaele Perego, and Salvatore Trani. X-dart: Blending dropouts and pruning for eﬃcient learning to rank. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017.