Post-Learning Optimization of Tree Ensembles for Efficient Ranking

Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ranking functions from a ground-truth of training examples. In practice, efficiency and effectiveness are intertwined concepts and trading off effectiveness for meeting efficiency constraints typically existing in large-scale systems is one of the most urgent issues. In this paper we propose a new framework, named CLEaVER, for optimizing machine-learned ranking models based on ensembles of regression trees. The goal is to improve efficiency at document scoring time without affecting quality. Since the cost of an ensemble is linear in its size, CLEaVER first removes a subset of the trees in the ensemble, and then fine-tunes the weights of the remaining trees according to any given quality measure. Experiments conducted on two publicly available LtR datasets show that CLEaVER is able to prune up to 80% of the trees and provides an efficiency speed-up up to 2.6x without affecting the effectiveness of the model.