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MatrixFactorizationtechniquesforrecommendersystems
CataloguePaperBackgroundIntroductionRecommenderSystemsStrategiesMatrixFactorizationMethodsABasicMatrixFactorizationModelLearningAlgorithmAddingBiasesAddingInputSourcesTemporalDynamicsInputWithVaryingConfidenceLevelsNetflixPrizeCompetitionConclusionTuesday,July2,20242
1、PaperBackgroundTuesday,July2,202431.YehudaKoren,YahooResearch2.RobertBellandChrisVolinsky,ATTLabs-Research3.PaperpublishedbytheIEEEComputerSocietyinAugust20094.AuthorwonthegrandNetflixPrizeCompetitioninSeptember2009
2、IntroductionModernconsumersareinundatedwithchoices.MoreretailorhavebecomeinterestedinRS,whichanalyzepatternsofuserinterestinproductstopridepersonalizedrecommendationsthatsuitauserstaste.NetflixandAhavemadeRSasalientpartoftheirwebsites.Particularlyuserfulforentainmentproductssuchasmovies,music,andTVshows.
3、RecommenderSystemStrategiesContentFilteringCollaborativeFiltering1.Neighborhoodmethods?user-oriented?item-oriented2.LatentFatorModelTuesday,July2,20245
3.1、ContentsFilteringCreateaprofileforeachuserorproducttocharacterizeitsnature.Needtogatherexternalinformation.AknownsuccessfulrealizationofcontentfilteringistheMusicGenomeProject,whichisusedfortheInternetradioserviceP.Tuesday,July2,20246
3.2、CollaborativeFilteringAnalyzerelationshipsbetweenusersandinterdep-enciesamongproductstoidentifynewuser-itemas-Socitions.Disadvantages:coldstartTwoprimaryareas:neighborhoodmethodsuser-orienteditem-orientedLatentfactormodelsTuesday,July2,20247
3.2.1、NeighborhoodmethodsCenteredoncomputingtherelationshipsbetweenitemsorusers.Theitem-orientedapproachevaluatesauser’spreferenceforanitembasedonratingsof“neighboring”itemsbythesameuser.Theuser-orientedappr
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