矩阵分解技术应用到推荐系统

矩阵分解技术应用到推荐系统第一页，编辑于星期六：二十三点二十二分。CataloguePaperBackgroundIntroductionRecommenderSystemsStrategiesMatrixFactorizationMethodsABasicMatrixFactorizationModelLearningAlgorithmAddingBiasesAddingInputSourcesTemporalDynamicsInputWithVaryingConfidenceLevelsNetflixPrizeCompetitionConclusionTuesday,April25,20232第二页，编辑于星期六：二十三点二十二分。1、PaperBackgroundTuesday,April25,202331.YehudaKoren,YahooResearch2.RobertBellandChrisVolinsky,AT&TLabs-Research3.PaperpublishedbytheIEEEComputerSocietyinAugust20094.AuthorwonthegrandNetflixPrizeCompetitioninSeptember2009第三页，编辑于星期六：二十三点二十二分。2、IntroductionModernconsumersareinundatedwithchoices.MoreretailorhavebecomeinterestedinRS,whichanalyzepatternsofuserinterestinproductstopridepersonalizedrecommendationsthatsuitauser'staste.NetflixandAhavemadeRSasalientpartoftheirwebsites.Particularlyuserfulforentainmentproductssuchasmovies,music,andTVshows.第四页，编辑于星期六：二十三点二十二分。3、RecommenderSystemStrategiesContentFilteringCollaborativeFiltering

1.Neighborhoodmethods

user-oriented

item-oriented

2.LatentFatorModelTuesday,April25,20235第五页，编辑于星期六：二十三点二十二分。3.1、ContentsFilteringCreateaproeachuserorproducttocharacterizeitsnature.Needtogatherexternalinformation.AknownsuccessfulrealizationofcontentfilteringistheMusicGenomeProject,whichisusedfortheInternetradioserviceP.Tuesday,April25,20236第六页，编辑于星期六：二十三点二十二分。3.2、CollaborativeFilteringAnalyzerelationshipsbetweenusersandinterdep-enciesamongproductstoidentifynewuser-itemas-Socitions.Disadvantages:coldstartTwoprimaryareas:neighborhoodmethodsuser-orienteditem-orientedLatentfactormodelsTuesday,April25,20237第七页，编辑于星期六：二十三点二十二分。3.2.1、NeighborhoodmethodsCenteredoncomputingtherelationshipsbetweenitemsor

users.Theitem-orientedapproachevaluatesa

user’spreferenceforanitembasedonratingsof“neighboring”itemsbythesameuser.Theuser-orientedapproachidentifieslike-mindeduserswhocancomplementeachother’sratings.Tuesday,April25,20238第八页，编辑于星期六：二十三点二十二分。Example:第九页，编辑于星期六：二十三点二十二分。3.2.2、LatentFactorModelsFindfeaturesthatdescribethecharacteristicsofratedobjects.Itemcharacteristicsanduserpreferencesaredescribedwithnumericalfactorvalues.Assumption:Ratingscanbeinferredfromamodelputtogetherfromasmallernumberofparameters.Tuesday,April25,202310第十页，编辑于星期六：二十三点二十二分。4、MatrixFactorizationMethodsCharacterizebothitemsandusersbyvectorsoffactorsinferredfromitemratingpatterns.RSrelyondifferenttypesofinputdata.Strength:incorporationofadditionalinformation,implicitfeedback.Implicitfeedback:purchasehistory,browsinghistory,searchpatterns,mousemovementandsoon.Tuesday,April25,202311第十一页，编辑于星期六：二十三点二十二分。5、ABasicMatrixFactorizationModelDotproductcapturestheuser’sestimatedinterestintheitem:(1)Here，theelementsofmeasuretheextenttowhichtheitempossessesthosefactors,theelementsofmeasuretheextentofinteresttheuserhasinitemsthatarehighonthecorrespondingfactors.Challenge:Howtocomputeamappingofitemsandusersfactorvectors?Approaches:SingularValueDecompositionn(SVD)

Tuesday,April25,202312第十二页，编辑于星期六：二十三点二十二分。5.1、SingularValueDecompositionRequirefactoringtheuser-itemratingmatrixConventionalSVDisundefinedforincompleteImputationtofillinmissingvaluesIncreasestheamountofdataModelingdirectlytheobservedratingsWeneedtoapproachthatcansimplyignoremissingvalue

第十三页，编辑于星期六：二十三点二十二分。5.1、SingularValueDecompositionMeasures:aregularizedmodel(2)Here,isthesetofthe(u,i)pairsforwhichisknown(thetrainingset);theconstantcontrolstheextentofregularization,determinedbycross-validation.第十四页，编辑于星期六：二十三点二十二分。6、LearningAlgorithmsTwomethodstominizingEquation(2)

StochasticGradientDescent

AlteringLeastSquaresTuesday,April25,202315第十五页，编辑于星期六：二十三点二十二分。6.1、StochasticGradientDescentLoopthroughallratingsinthetrainingsetForeachgiventraingcase,thesystempredictsandcomputestheassociatedpredictionerrorBymagnitudeproportionaltointheoppositedirectionofthegradient

第十六页，编辑于星期六：二十三点二十二分。6.2、AlternatingLeastSquaresALSteachniquesrotatebetweenfixingtheandfixingtheALSisfavorableinatleasttwocases:AllowsmassiveparallelizationCenteredonimplicitdata

第十七页，编辑于星期六：二十三点二十二分。7、AddingBiasesAfirst-orderapproximationofthebiasinvolvedinratingisasfollows:（3）Here,istheoverallaverage;theparameters，indicatetheobserveddeviationsofuseranditemi.Includingbiasparametersintheprediction:（4)Optimize:(5)Tuesday,April25,202318第十八页，编辑于星期六：二十三点二十二分。8、AddingInputSourcesProblem:coldstartSolution:incorporateadditionalsourcesofinformationabouttheusers.Twoinformation:itemattributes,userattributesItemattribute：NormalizingthesumUserattribute:Optimaion:(6)

第十九页，编辑于星期六：二十三点二十二分。9、TemporalDynamicsRatingmaybeaffectedbytemporaleffectsPopularityofanitemmaychangeUser'sidentityandpreferencesmaychangeModelingtemporalaffectscanimporveaccuracysignificantlyRatingpredictionsasafunctionoftime:

(7)Tuesday,April25,202320第二十页，编辑于星期六：二十三点二十二分。10、InputwithVaringConfidencelevelsInseveralsetups,notallobservedratingsdeservethesameweightorconfidence.Plan:ConfidenceinobservingisdenotedasCostfounction:

（8）Tuesday,April25,202321第二十一页，编辑于星期六：二十三点二十二分。11、Ne