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1、FactorizationMeetstheNeighborhood:aMultifacetedCollaborativeFilteringModelYehudaKorenAT&TLabs–Research180ParkAve,FlorhamPark,NJ07932yehuda@research.att.comABSTRACTprevioustransactionsorproductratings—anddoesnotrequirethecreationofexplicitprofiles.Notably,CFtechniquesrequire
2、nodo-Recommendersystemsprovideuserswithpersonalizedsuggestionsmainknowledgeandavoidtheneedforextensivedatacollection.forproductsorservices.ThesesystemsoftenrelyonCollaborat-Inaddition,relyingdirectlyonuserbehaviorallowsuncoveringingFiltering(CF),wherepasttransactionsareana
3、lyzedinordertocomplexandunexpectedpatternsthatwouldbedifficultorimpos-establishconnectionsbetweenusersandproducts.Thetwomoresibletoprofileusingknowndataattributes.Asaconsequence,CFsuccessfulapproachestoCFarelatentfactormodels,whichdi-attractedmuchofattentioninthepastdecade,r
4、esultinginsignif-rectlyprofilebothusersandproducts,andneighborhoodmodels,icantprogressandbeingadoptedbysomesuccessfulcommercialwhichanalyzesimilaritiesbetweenproductsorusers.Inthisworksystems,includingAmazon[15],TiVoandNetflix.weintroducesomeinnovationstobothapproaches.Thefa
5、ctorandInordertoestablishrecommendations,CFsystemsneedtocom-neighborhoodmodelscannowbesmoothlymerged,therebybuild-parefundamentallydifferentobjects:itemsagainstusers.Thereareingamoreaccuratecombinedmodel.Furtheraccuracyimprove-twoprimaryapproachestofacilitatesuchacompariso
6、n,whichcon-mentsareachievedbyextendingthemodelstoexploitbothexplicitstitutethetwomaindisciplinesofCF:theneighborhoodapproachandimplicitfeedbackbytheusers.Themethodsaretestedontheandlatentfactormodels.Netflixdata.ResultsarebetterthanthosepreviouslypublishedonNeighborhoodmeth
7、odsarecenteredoncomputingtherelation-thatdataset.Inaddition,wesuggestanewevaluationmetric,whichshipsbetweenitemsor,alternatively,betweenusers.Anitem-highlightsthedifferencesamongmethods,basedontheirperfor-orientedapproachevaluatesthepreferenceofausertoanitemmanceatatop-Kre
8、commendationtask.basedonratingsofsimilaritemsbythesameuser.Inasense,CategoriesandSubjectD
