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Learning to Rank on a Cluster using Boosted Decision Trees

Learning to Rank on a Cluster using Boosted Decision Trees. Krysta M. Svore , Christopher J.C. Burges Microsoft Research. Motivation. Growing number of extremely large datasets Terabytes/day of search user interaction data Cheaper hardware

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Learning to Rank on a Cluster using Boosted Decision Trees

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  1. Learning to Rank on a Cluster using Boosted Decision Trees Krysta M. Svore, Christopher J.C. Burges Microsoft Research

  2. Motivation • Growing number of extremely large datasets • Terabytes/day of search user interaction data • Cheaper hardware • Decreased model training time allows for solutions to more problems (faster throughput) • Distribute LambdaMART[Wu et al., 2009] • Gradient boosted decision trees, where gradients are approximated using λ-functions • Constructed incrementally # boosting iterations; sample feature vector; weak hypothesis • Key component of winning system in Yahoo! Learning to Rank Challenge

  3. Our Approach • Feature distribution • Data fits in main memory of a single machine • Tree split computations are distributed • Learned model equivalent to centralized model • Data distribution • Data does not fit in main memory of a single machine • Data is distributed and used for cross-validation • Learned model not equivalent to centralized model • Two strategies for selection of next weak hypothesis (full and sample)

  4. Time Comparison • Two types of cluster nodes. • Features split across nodes. • Each node stores all of the training data. • Centralized (dotted) • Feature-distributed (solid) Feature distribution yields 6 times speed-up

  5. Time Comparison • Data split across nodes. • Each node stores 3500 queries. (Centralized stores 3500*K queries). • Centralized (dotted) • Full data-distributed (solid) • Sample data-distributed (dashed) Data distribution yields 10 times speed-up

  6. Accuracy Comparison • Data split across nodes. • Each node stores 3500 queries. • Full data-distributed (solid) • Sample data-distributed (dashed) Data distribution yields significant accuracy gain when data size exceeds main memory size.

  7. Accuracy Comparison • Data split across nodes. • Each node stores 3500 queries. (Centralized stores 3500*K queries). • Centralized (dotted) • Full data-distributed (solid) • Sample data-distributed (dashed) Centralized model significantly better than data-distributed model. More training data better than massive cross-validation.

  8. Future Work • Performing massive, distributed cross-validation results in significant accuracy loss compared to learning from centralized training data. • Can we distribute data and achieve equal/better accuracy while decreasing training time? • Can we develop an asynchronous approach resulting in additional speed improvements? Thanks!

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