Large-scale Learning to Rank using Boosted Decision Trees

This work investigates the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs, and implements a method for improving the speed of training when the training data size exceeds the main memory of a single machine.