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1、FactorintheNeighbors:ScalableandAccurateCollaborativeFilteringYehudaKoren∗Yahoo!Research,Haifayehuda@yahoo-inc.comABSTRACTandlatentfactormodels.Neighborhoodmethodsarecenteredoncomputingtherelation-Recommendersystemsprovideuserswithpersonalizedsuggestionsshipsbetweenitemsor,
2、alternatively,betweenusers.Anitem-itemforproductsorservices.ThesesystemsoftenrelyonCollaboratingapproachevaluatesthepreferenceofausertoanitembasedonFiltering(CF),wherepasttransactionsareanalyzedinordertoes-ratingsofsimilaritemsbythesameuser.Inasense,thesemeth-tablishconnect
3、ionsbetweenusersandproducts.ThemostcommonodstransformuserstotheitemspacebyviewingthemasbasketsapproachtoCFisbasedonneighborhoodmodels,whichisbasedofrateditems.Thisway,wenolongerneedtocompareuserstoonsimilaritiesbetweenproductsorusers.Inthisworkweintroduceitems,butratherdire
4、ctlyrelateitemstoitems.anewneighborhoodmodelwithanimprovedpredictionaccuracy.Latentfactormodels,suchasSingularValueDecomposition(SVD),Unlikepreviousapproachesthatarebasedonheuristicsimilarities,compriseanalternativeapproachbytransformingbothitemsandwemodelneighborhoodrelati
5、onsbyminimizingaglobalcostfunc-userstothesamelatentfactorspace,thusmakingthemdirectlytion.Furtheraccuracyimprovementsareachievedbyextendingthecomparable.Thelatentspacetriestoexplainratingsbycharacteriz-modeltoexploitbothexplicitandimplicitfeedbackbytheusers.ingbothproductsa
6、ndusersonfactorsautomaticallyinferredfromPastmodelswerelimitedbytheneedtocomputeallpairwisesimi-userfeedback.Forexample,whentheproductsaremovies,fac-laritiesbetweenitemsorusers,whichgrowquadraticallywithinputtorsmightmeasureobviousdimensionssuchascomedyvs.drama,size.Inparti
7、cular,thislimitationvastlycomplicatesadoptinguseramountofaction,ororientationtochildren;lesswelldefineddi-similaritymodels,duetothetypicallargenumberofusers.Ourmensionssuchasdepthofcharacterdevelopmentor“quirkiness”;newmodelsolvestheselimitationsbyfactoringtheneighborhoodorc
8、ompletelyuninterpretabledimensions.Forusers,eachfactormodel,thusmakingbothitem-ite