{"id":160619,"date":"2010-12-01T00:00:00","date_gmt":"2010-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/learning-to-rank-on-a-cluster-using-boosted-decision-trees\/"},"modified":"2018-10-16T20:28:12","modified_gmt":"2018-10-17T03:28:12","slug":"learning-to-rank-on-a-cluster-using-boosted-decision-trees","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-to-rank-on-a-cluster-using-boosted-decision-trees\/","title":{"rendered":"Learning to Rank on a Cluster using Boosted Decision Trees"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs, and a boosted tree ranking algorithm called LambdaMART. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in order to evaluate the loss or gain incurred by the distributed algorithms we consider. Our contributions are two-fold: (1) we implement a method for improving the speed of training when the training data fits in main memory on a single machine; (2) we develop a training method for the case where the training data size exceeds the main memory of a single machine that easily scales to far larger datasets, i.e., billions of examples, and is based on data distribution. Results of our methods on a real-world Web dataset indicate significant improvements in training speed.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs, and a boosted tree ranking algorithm called LambdaMART. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160619","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Learning to Rank on Cores, Clusters, and Clouds Workshop at NIPS 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