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1、ExpertSystemswithApplications37(2010)5599–5606ContentslistsavailableatScienceDirectExpertSystemswithApplicationsjournalhomepage:www.elsevier.com/locate/eswaUsingevidencebasedcontenttrustmodelforspamdetectiona,b,ca,b,cd,*WeiWang,GuosunZeng,DaizhongTangaDepartmentofComputerScienceandEng
2、ineering,TongjiUniversity,Shanghai200092,ChinabTongjiBranchNationalEngineeringandTechnologyCenterofHighPerformance,Shanghai200092,ChinacKeyLaboratoryofEmbeddedSystemandServiceComputing,MinistryofEducation,Shanghai200092,ChinadSchoolofEconomicsandManagement,TongjiUniversity,Shanghai200
3、092,ChinaarticleinfoabstractKeywords:Contenttrustisoneofthemaincomponentsintheresearchofinformationretrieval.AsitgetseasiertoContenttrustaddinformationtotheWebviaHTMLpages,wikis,blogs,andotherdocuments,itgetstoughertodistin-Webspamguishaccurateortrustworthyinformationfrominaccurateoru
4、ntrustworthyinformationontheWeb.RankingCurrenttechnologyofspamdetectionisbasedonbinarymetric,thatisbinaryclassificationisadaptedSVMinthespamdetection.Inordertomeettheusers’needandpreference,moreaccuratemetricisneededMachinelearninginthecontenttrustaswellasindetectingspaminformation.Int
5、hispaper,weusethenotionofcontenttrustforspamdetection,andregarditasarankingproblem.Besidestraditionaltextfeatureattributes,informationqualitybasedevidenceisintroducedtodefinethetrustfeatureofspaminformation,andanovelcontenttrustlearningalgorithmbasedontheseevidenceisproposed.Finally,aW
6、ebspamdetec-tionsystemisdevelopedandtheexperimentsontherealWebdataarecarriedout,whichshowthepro-posedmethodperformsverywellinpractice.Ó2010ElsevierLtd.Allrightsreserved.1.Introductiontratingsearchexperiences.Second,ifausersearchesforinforma-tionthatisrelevanttoyourpagesbutyourpagesare
7、rankedlowInformationretrieval(IR)isthestudyofhelpinguserstofindbysearchengines,thentheusermaynotseethepagesbecauseinformationthatmatchestheirinformationneeds.Technically,oneseldomclicksalargenumberofreturnedpages.Finally,ainformationretrievalstudiestheacquisition,organization,storage,s
8、earch