document: Large-scale behavioral targeting Behavioral targeting ( BT ) leverages historical user behavior to select the ads most relevant to users to display . The state-of-the-art of BT derives a linear Poisson regression model from fine-grained user behavioral data and predicts click-through rate ( CTR ) from user history . We designed and implemented a highly scalable and efficient solution to BT using Hadoop MapReduce framework . With our parallel algorithm and the resulting system , we can build above 450 BT-category models from the entire Yahoo 's user base within one day , the scale that one can not even imagine with prior systems . Moreover , our approach has yielded 20 % CTR lift over the existing production system by leveraging the well-grounded probabilistic model fitted from a much larger training dataset . Specifically , our major contributions include : ( 1 ) A MapReduce statistical learning algorithm and implementation that achieve optimal data parallelism , task parallelism , and load balance in spite of the typically skewed distribution of domain data . ( 2 ) An in-place feature vector generation algorithm with linear time complexity O ( n ) regardless of the granularity of sliding target window . ( 3 ) An in-memory caching scheme that significantly reduces the number of disk IOs to make large-scale learning practical . ( 4 ) Highly efficient data structures and sparse representations of models and data to enable fast model updates . We believe that our work makes significant contributions to solving large-scale machine learning problems of industrial relevance in general . Finally , we report comprehensive experimental results , using industrial proprietary codebase and datasets .
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