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1、WWW2007/Track:DataMiningSession:PredictiveModelingofWebUsersDemographicPredictionBasedonUser’sBrowsingBehaviorJianHu,Hua-JunZeng,HuaLi,ChengNiu,ZhengChenMicrosoftResearchAsia49ZhichunRoadBeijing100080,P.R.China{jianh,hjzeng,huli,chengniu,zhengc}@microsoft.comABSTRACT
2、Yahoo!allowsuserstobuildtheirpreferencesexplicitlyandonlyshowsectionsanddetailswhichtheymaybeinterestedin.GoogleDemographicinformationplaysanimportantroleinpersonalizedPersonalorganizesusers’searchresultsaccordingtotheirsearchwebapplications.However,itisusuallynoteas
3、ytoobtainthishistoriesincludingtheirprevioussearchresultsandnewskindofpersonaldatasuchasageandgender.Inthispaper,weheadlinesclicked.Accompanywiththeprosperousofgeneralwebmadeafirstapproachtopredictusers’genderandagefromtheirservice,onlineadvertisingisgrowingrapidlyin
4、recentyears,inWebbrowsingbehaviors,inwhichtheWebpageviewwhichbehavioraltargetingisbecomingparticularlypopular[22].informationistreatedasahiddenvariabletopropagateBehaviortargetinghelpsadvertiserstotargetproperusersupondemographicinformationbetweendifferentusers.There
5、arethreetheirbehaviorswhilesurfingonline.Asreportedin[18],mainstepsinourapproach:First,learningfromtheWebpagecompanieslikeTacodaSystems,Claria,RevenueScienceandTMclick-thoughdata,Webpagesareassociatedwithusers’(known)Advertisingprovideadvertiserswithbehavioraltargeti
6、ngageandgendertendencythroughadiscriminativemodel;Second,technologies.AccordingtotherecentstudiesofTMAdvertising,users’(unknown)ageandgenderarepredictedfromthecomparedtosimplewebads,behavior-basedadsgain115%moredemographicinformationoftheassociatedWebpagesthroughabus
7、inesstrafficayear,andthetargetedconsumersalsoscored3%Bayesianframework;Third,basedonthefactthatWebpageshigherthantheaverageviewersinbrandawareness.Userprofilevisitedbysimilarusersmaybeassociatedwithsimilarwhichincludespriorsearchresults,demographicinformation,demogra
8、phictendency,anduserswithsimilardemographicgeographicinformationandinterestedtopicsplaysakeyroleininformationwouldvisitsimilarWebpa
