Optimizing ensembles of boosted additive bagged trees for learning-to-rank

Abstract

The task of retrieving information that really matters to the users is considered hard when taking into consideration the current and increasingly amount of available information. To improve the effectiveness of this information seeking task, systems have relied on the combination of many predictors by means of machine learning methods, a task also known as learning to rank (L2R). The most effective learning methods for this task are based on ensembles of trees. In this master degree dissertation, is proposed a general framework that smoothly combines ensembles of additive trees, specifically Random Forests, with Boosting in an original way for the task of L2R. In particular, we exploit a set of functions that enable us to smartly deduce the samples that are considered hard to predict in a regression approach and apply a set of selective weight updating strategy to effectively enhance the ranking performance.