Learning to Rank on a Cluster using Boosted Decision Trees

This work investigates the problem of learning to rank on a cluster of Web search data composed of 140,000 queries and approximately fourteen mil lion URLs, and a boosted tree ranking algorithm called LambdaMART, and implements a method for improving the speed of training when the training data fits in main memory on a single machine.