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 tress (e.g., Random Forests) and/or boosting techniques (e.g., RankBoost, MART, LambdaMART). In this paper, we propose a general framework that smoothly combines ensembles of additive trees, specifically Random Forests, with Boosting in a original way for the task of L2R. In particular, we exploit out-of-bag samples as well as a selective weight updating strategy (according to the out-of-bag samples) to effectively enhance the ranking performance. We instantiate such a general framework by considering different loss functions, different ways of weighting the weak learners as well as different types of weak learners. In our experiments our rankers were able to outperform all state-of-the-art baselines in all considered datasets, using just a small percentage of the original training set and faster convergence rates.
['Clebson C. A. de Sá', 'Marcos André Gonçalves', 'Daniel Xavier de Sousa', 'Thiago Salles']
Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests