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We present a vertical introduction to campaign optimization; that is, theability to predict the user response to an ad campaign without any users'profiles on average and for each exposed ad. In practice, we present anapproach to build a polytomous model, multi response, composed by severalhundred binary models using generalized linear models. The theory has beenintroduced twenty years ago and it has been applied in different fields sincethen. Here, we show how we optimize hundreds campaigns and how this largenumber of campaigns may overcome a few characteristic caveats of singlecampaign optimization. We discuss the problem and solution of training andcalibration at scale. We present statistical performance as {\em coverage},{\em precision} and {\em recall} used in classification. We present also adiscussion about the potential performance as throughput: how many decisionscan be done per second streaming the bid auctions also by using dedicatedhardware.

Multiple-Campaign Ad-Targeting Deployment: Parallel Response Modeling, Calibration and Scoring Without Personal User Information