矩陣分解技術(shù)應(yīng)用到系統(tǒng)課件

MatrixFactorizationtechniquesforrecommendersystemsCataloguePaperBackgroundIntroductionRecommenderSystemsStrategiesMatrixFactorizationMethodsABasicMatrixFactorizationModelLearningAlgorithmAddingBiasesAddingInputSourcesTemporalDynamicsInputWithVaryingConfidenceLevelsNetflixPrizeCompetitionConclusionWednesday,April17,202421、PaperBackgroundWednesday,April17,202431.YehudaKoren,YahooResearch2.RobertBellandChrisVolinsky,AT&TLabs-Research3.PaperpublishedbytheIEEEComputerSocietyinAugust20094.AuthorwonthegrandNetflixPrizeCompetitioninSeptember20092、IntroductionModernconsumersareinundatedwithchoices.MoreretailorhavebecomeinterestedinRS,whichanalyzepatternsofuserinterestinproductstopridepersonalizedrecommendationsthatsuitauser'staste.NetflixandAhavemadeRSasalientpartoftheirwebsites.Particularlyuserfulforentainmentproductssuchasmovies,music,andTVshows.3、RecommenderSystemStrategiesContentFilteringCollaborativeFiltering

1.Neighborhoodmethods

user-oriented

item-oriented

2.LatentFatorModelWednesday,April17,202453.1、ContentsFilteringCreateaprofileforeachuserorproducttocharacterizeitsnature.Needtogatherexternalinformation.AknownsuccessfulrealizationofcontentfilteringistheMusicGenomeProject,whichisusedfortheInternetradioserviceP.Wednesday,April17,202463.2、CollaborativeFilteringAnalyzerelationshipsbetweenusersandinterdep-enciesamongproductstoidentifynewuser-itemas-Socitions.Disadvantages:coldstartTwoprimaryareas:neighborhoodmethodsuser-orienteditem-orientedLatentfactormodelsWednesday,April17,202473.2.1、NeighborhoodmethodsCenteredoncomputingtherelationshipsbetweenitemsor

users.Theitem-orientedapproachevaluatesa

user’spreferenceforanitembasedonratingsof“neighboring”itemsbythesameuser.Theuser-orientedapproachidentifieslike-mindeduserswhocancomplementeachother’sratings.Wednesday,April17,20248Example:3.2.2、LatentFactorModelsFindfeaturesthatdescribethecharacteristicsofratedobjects.Itemcharacteristicsanduserpreferencesaredescribedwithnumericalfactorvalues.Assumption:Ratingscanbeinferredfromamodelputtogetherfromasmallernumberofparameters.Wednesday,April17,2024104、MatrixFactorizationMethodsCharacterizebothitemsandusersbyvectorsoffactorsinferredfromitemratingpatterns.RSrelyondifferenttypesofinputdata.Strength:incorporationofadditionalinformation,implicitfeedback.Implicitfeedback:purchasehistory,browsinghistory,searchpatterns,mousemovementandsoon.Wednesday,April17,2024115、ABasicMatrixFactorizationModelDotproductcapturestheuser’sestimatedinterestintheitem:(1)Here，theelementsofmeasuretheextenttowhichtheitempossessesthosefactors,theelementsofmeasuretheextentofinteresttheuserhasinitemsthatarehighonthecorrespondingfactors.Challenge:Howtocomputeamappingofitemsandusersfactorvectors?Approaches:SingularValueDecompositionn(SVD)

Wednesday,April17,2024125.1、SingularValueDecompositionRequirefactoringtheuser-itemratingmatrixConventionalSVDisundefinedforincompleteImputationtofillinmissingvaluesIncreasestheamountofdataModelingdirectlytheobservedratingsWeneedtoapproachthatcansimplyignoremissingvalue

5.1、SingularValueDecompositionMeasures:aregularizedmodel(2)Here,isthesetofthe(u,i)pairsforwhichisknown(thetrainingset);theconstantcontrolstheextentofregularization,determinedbycross-validation.6、LearningAlgorithmsTwomethodstominizingEquation(2)

StochasticGradientDescent

AlteringLeastSquaresWednesday,April17,2024156.1、StochasticGradientDescentLoopthroughallratingsinthetrainingsetForeachgiventraingcase,thesystempredictsandcomputestheassociatedpredictionerrorBymagnitudeproportionaltointheoppositedirectionofthegradient

6.2、AlternatingLeastSquaresALSteachniquesrotatebetweenfixingtheandfixingtheALSisfavorableinatleasttwocases:AllowsmassiveparallelizationCenteredonimplicitdata

7、AddingBiasesAfirst-orderapproximationofthebiasinvolvedinratingisasfollows:（3）Here,istheoverallaverage;theparameters，indicatetheobserveddeviationsofuseranditemi.Includingbiasparametersintheprediction:（4)Optimize:(5)Wednesday,April17,2024188、AddingInputSourcesProblem:coldstartSolution:incorporateadditionalsourcesofinformationabouttheusers.Twoinformation:itemattributes,userattributesItemattribute：NormalizingthesumUserattribute:Optimaion:(6)

9、TemporalDynamicsRatingmaybeaffectedbytemporaleffectsPopularityofanitemmaychangeUser'sidentityandpreferencesmaychangeModelingtemporalaffectscanimporveaccuracysignificantlyRatingpredictionsasafunctionoftime:

(7)Wednesday,April17,20242010、InputwithVaringConfidencelevelsInseveralsetups,notallobservedratingsdeservethesameweightorconfidence.Plan:ConfidenceinobservingisdenotedasCostfounction:

（8）