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1、Web-ScaleUserModelingforTargetingMohamedAly,AndrewHatch,VanjaJosifovski,VijayK.NarayananYahoo!ResearchSantaClara,CA95051,USA{aly,aohatch,vanjaj,vnarayan}@yahoo-inc.comABSTRACTpossible.Intheonlineenvironment,adplatformshavetra-ditionallyallowedadvertiserstotargetusersinpopulationsWepresentt
2、heexperiencesfrombuildingaweb-scaleuserthatarelikelytorespondpositivelytotheprovidedadvertis-modelingplatformforoptimizingdisplayadvertisingtar-ing.TraditionallythemethodstospecifyuserpopulationsgetingatYahoo!.Theplatformdescribedinthispaperal-relyondemographicsorlocationinfo,wheretheadver
3、tiserlowsforper-campaignmaximizationofconversionsrepre-choosesasetoflocationsandcertaindemographicattributessentingpurchaseactivitiesortransactions.Conversionsdi-toselectthepopulationsegmenttoseethead.Thedemo-rectlytranslatetoadvertiser'srevenue,andthusprovidethegraphicsandlocationattribut
4、escaneitherbeobtainedfrommostrelevantmetricsofreturnonadvertisinginvestment.registrationdatabasesorinferredfrompastuseractivity.Wefocusontwomajorchallenges:howtoecientlypro-Anothertraditionalonlinetargetingmethodthatusesthecesshistoriesofbillionsofusersonadailybasis,andhowtopastuseractivi
5、tyisbehavioraltargeting.Here,insteadofbuildper-campaignconversionmodelsgiventheextremelybucketingtheusersintodemographics'categories,usersarelowconversionrates(comparedtoclickratesinatraditionalputintopredenedinterestcategoriesasparenting,auto,setting).Werstpresentmechanismsforbuildingwe
6、b-scalehealth,whicharethenpurchasedbytheadvertisers.userprolesinadailyincrementalfashion.Second,weshowOneweaknessofthedemographicandbehavioraltarget-howtoreducethelatencythroughin-memoryprocessingofingisthatthesegmentsofusersarenotcreatedusinganybillionsofuserrecords.Finally,wediscussatec
7、hniqueforfeedbackfromindividualadvertisersandusuallyrepresentscalingthenumberofhandledcampaigns/modelsbyintro-largeinterestgroups.Forexample,atypicalbehavioralcat-ducinganecientlabelingtechniquethatallowsforsharingegorywouldbehealthandtness".Suchcategorywil