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1、ImprovingCollaborativeFilteringwithTrust-basedMetricsJianshuWengChunyanMiaoAngelaGohSchoolofComputerEngineering,NanyangTechnologicalUniversity,Singapore{WENG0004,ASCYMiao,ASESGoh}@ntu.edu.sgABSTRACTitems’ratings,leadingtolowcoverage.•Similarity-basedCFisusuallysusceptibletoattacks.Des
2、piteitssuccess,similarity-basedcollaborativefilteringForexample,asthenumberofsimilarusersisusuallysuffersfromsomesignificantlimitations,suchasscalability1andsparsity.Thispaperintroducestrusttothedomainofsmall,malicioususers’noisyratingscaneasilyout-collaborativefilteringtoovercometheselim
3、itations.Com-weighotherusers’ratings.Asaresult,presenceofnoisyratingswillbiasthepredictions.paredwiththesimilarity-basedCF,introductionoftrustThispaperpresentsoureffortstoovercometheselimita-doesimprovetheperformanceofCFintermsofcoverage,tions.predictionaccuracy,androbustnessintheprese
4、nceofat-Itisproposedthateachusermaintainsratingsindivid-tacks.Experimentalresultsbasedonarealdatasetareil-ually.Eachuseristreatedequallyasapeerandconnectslustratedasevidencestosupportourclaim.witheachothertoformacollaborativeinformationfilteringCategoriesandSubjectDescriptors:H.3.3[Inf
5、orma-community,whichisinfactaP2Poverlaynetwork.ThetionSearchandRetrieval]:InformationfilteringP2P-basedarchitectureisthenexploitedtofacilitateusers’GeneralTerms:Algorithm,Performancerecommendationscollection.TheP2P-basedarchitectureexemptstheneedforacentralizedserver,leadingtohigherKey
6、words:CollaborativeFiltering,Trustscalability.1.INTRODUCTIONFurthermore,atrustmetricsisdesignedwhichhelpsaCollaborativefiltering(CF),whichpredictsnewitems’usertoquantifythedegreeoftrustitshouldplaceonaratingsforactiveusersbasedonotherusers’ratings,isoneparticularuser.Thetrustmetricsisc
7、omputableonmostofthemostsuccessfultechniquesontheweb.Traditionally,users,evenonpairsofuserswhohaveonlyco-ratedoneCFissimilarity-based,i.e.thepredictionsaremadebasedcommonitem.Auserisalsoabletoestablishtrustviatrustonsimilarusers’ratings[8].Theuser-ratingdataisusuallypropagationonusers
8、withw