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1、LatentSemanticModelsforCollaborativeFilteringTHOMASHOFMANNBrownUniversityCollaborativefilteringaimsatlearningpredictivemodelsofuserpreferences,interestsorbehaviorfromcommunitydata,thatis,adatabaseofavailableuserpreferences.Inthisarticle,wedescribeanewfamilyofmodel-bas
2、edalgorithmsdesignedforthistask.Thesealgorithmsrelyonastatisticalmodellingtechniquethatintroduceslatentclassvariablesinamixturemodelsettingtodiscoverusercommunitiesandprototypicalinterestprofiles.Weinvestigateseveralvariationstodealwithdiscreteandcontinuousresponsevar
3、iablesaswellaswithdifferentobjectivefunctions.Themainadvantagesofthistechniqueoverstandardmemory-basedmethodsarehigheraccuracy,constanttimeprediction,andanexplicitandcompactmodelrepresentation.Thelattercanalsobeusedtomineforusercommunitites.Theexperimentalevaluations
4、howsthatsubstantialimprovementsinaccucracyoverexistingmethodsandpublishedresultscanbeobtained.CategoriesandSubjectDescriptors:H.3.3[InformationStorageandRetrieval]:InformationSearchandRetrieval—informationfiltering;I.5.3[PatternRecognition]:Clustering—algorithmsGenera
5、lTerms:Collaborativefiltering,recommendersystems,machinelearning,mixturemodels,latentsemanticanalysis1.INTRODUCTIONContent-basedfilteringandretrievalbuildsonthefundamentalassumptionthatusersareabletoformulatequeriesthatexpresstheirinterestsorinfor-mationneedsintermofin
6、trinsicfeaturesoftheitemssought.Insomecases,however,itmaybedifficulttoidentifysuitabledescriptorssuchaskeywords,topics,genres,etc.thatcanbeusedtoaccuratelydescribeinterests.Yetinothercases,forexample,inelectroniccommerce,usersmaybeunawareoratleastinattentiveoftheirint
7、erest.Inbothcases,onewouldliketopredictuserprefer-encesandrecommenditemswithoutrequiringtheusertoexplicitlyformulateaquery.Collaborativefilteringisatechnologythatiscomplementraytocontent-basedfilteringandthataimsatlearningpredictivemodelsofuserpreferences,Thisworkwassp
8、onsoredbyNSF-ITRgrants,awardnumbersIIS-0085836andIIS-0085940.Author’saddress:DepartmentofComputerScience,Box1910,BrownUniversity,Pr