Learning to rank is a machine learning technique broadly used in many areas such as document retrieval, collaborative filtering or question answering. We present experimental results which suggest that the performance of the current state-of-the-art learning to rank algorithm LambdaMART, when used for document retrieval for search engines, can be improved if standard regression trees are replaced by oblivious trees. This paper provides a comparison of both variants and our results demonstrate that the use of oblivious trees can improve the performance by more than 2:2%. Additional experimental analysis of the inuence of a number of features and of a size of the training set is also provided and confirms the desirability of properties of oblivious decision trees.

About the Speaker: Dr Michal Ferov is a Postdoctoral Research Fellow in the School of Mathematical and Physical Sciences,Faculty of Science and Information Technology.