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。第1页，共25页。CataloguePaperBackgroundIntroductionRecommenderSystemsStrategiesMatrixFactorizationMethodsABasicMatrixFactorizationModelLearningAlgorithmAddingBiasesAddingInputSourcesTemporalDynamicsInputWithVaryingConfidenceLevelsNetflixPrizeCompetitionConclusionFriday,May12,20232第二页，编辑于星期六：二十三点二十二分。第2页，共25页。1、PaperBackgroundFriday,May12,202331.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。第3页，共25页。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。第4页，共25页。3、RecommenderSystemStrategiesContentFilteringCollaborativeFiltering

1.Neighborhoodmethods

user-oriented

item-oriented

2.LatentFatorModelFriday,May12,20235第五页，编辑于星期六：二十三点二十二分。第5页，共25页。3.1、ContentsFilteringCreateaproeachuserorproducttocharacterizeitsnature.Needtogatherexternalinformation.AknownsuccessfulrealizationofcontentfilteringistheMusicGenomeProject,whichisusedfortheInternetradioserviceP.Friday,May12,20236第六页，编辑于星期六：二十三点二十二分。第6页，共25页。3.2、CollaborativeFilteringAnalyzerelationshipsbetweenusersandinterdep-enciesamongproductstoidentifynewuser-itemas-Socitions.Disadvantages:coldstartTwoprimaryareas:neighborhoodmethodsuser-orienteditem-orientedLatentfactormodelsFriday,May12,20237第七页，编辑于星期六：二十三点二十二分。第7页，共25页。3.2.1、NeighborhoodmethodsCenteredoncomputingtherelationshipsbetweenitemsor

users.Theitem-orientedapproachevaluatesa

user’spreferenceforanitembasedonratingsof“neighboring”itemsbythesameuser.Theuser-orientedapproachidentifieslike-mindeduserswhocancomplementeachother’sratings.Friday,May12,20238第八页，编辑于星期六：二十三点二十二分。第8页，共25页。Example:第九页，编辑于星期六：二十三点二十二分。第9页，共25页。3.2.2、LatentFactorModelsFindfeaturesthatdescribethecharacteristicsofratedobjects.Itemcharacteristicsanduserpreferencesaredescribedwithnumericalfactorvalues.Assumption:Ratingscanbeinferredfromamodelputtogetherfromasmallernumberofparameters.Friday,May12,202310第十页，编辑于星期六：二十三点二十二分。第10页，共25页。4、MatrixFactorizationMethodsCharacterizebothitemsandusersbyvectorsoffactorsinferredfromitemratingpatterns.RSrelyondifferenttypesofinputdata.Strength:incorporationofadditionalinformation,implicitfeedback.Implicitfeedback:purchasehistory,browsinghistory,searchpatterns,mousemovementandsoon.Friday,May12,202311第十一页，编辑于星期六：二十三点二十二分。第11页，共25页。5、ABasicMatrixFactorizationModelDotproductcapturestheuser’sestimatedinterestintheitem:(1)Here，theelementsofmeasuretheextenttowhichtheitempossessesthosefactors,theelementsofmeasuretheextentofinteresttheuserhasinitemsthatarehighonthecorrespondingfactors.Challenge:Howtocomputeamappingofitemsandusersfactorvectors?Approaches:SingularValueDecompositionn(SVD)

Friday,May12,202312第十二页，编辑于星期六：二十三点二十二分。第12页，共25页。5.1、SingularValueDecompositionRequirefactoringtheuser-itemratingmatrixConventionalSVDisundefinedforincompleteImputationtofillinmissingvaluesIncreasestheamountofdataModelingdirectlytheobservedratingsWeneedtoapproachthatcansimplyignoremissingvalue

第十三页，编辑于星期六：二十三点二十二分。第13页，共25页。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。第14页，共25页。6、LearningAlgorithmsTwomethodstominizingEquation(2)

StochasticGradientDescent

AlteringLeastSquaresFriday,May12,202315第十五页，编辑于星期六：二十三点二十二分。第15页，共25页。6.1、StochasticGradientDescentLoopthroughallratingsinthetrainingsetForeachgiventraingcase,thesystempredictsandcomputestheassociatedpredictionerrorBymagnitudeproportionaltointheoppositedirectionofthegradient

第十六页，编辑于星期六：二十三点二十二分。第16页，共25页。6.2、AlternatingLeastSquaresALSteachniquesrotatebetweenfixingtheandfixingtheALSisfavorableinatleasttwocases:AllowsmassiveparallelizationCenteredonimplicitdata

第十七页，编辑于星期六：二十三点二十二分。第17页，共25页。7、AddingBiasesAfirst-orderapproximationofthebiasinvolvedinratingisasfollows:（3）Here,istheoverallaverage;theparameters，indicatetheobserveddeviationsofuseranditemi.Includingbiasparametersintheprediction:（4)Optimize:(5)Friday,May12,202318第十八页，编辑于星期六：二十三点二十二分。第18页，共25页。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。第19页，共25页。9、TemporalDynamicsRatingmaybeaffectedbytemporaleffectsPopularityofanitemmaychangeUser'sidentityandpreferencesmaychangeModelingtemporalaffectscanimporveaccuracysignificantlyRatingpredictionsasafunctionoftime:

(7)Friday,May12,202320第二十页，编辑于星期六：二十三点二十二分。第20页，共25页。10、InputwithVaringConfidencelevelsInseveralsetups,notallobservedratingsdeservethesameweightorconfidence.Plan:ConfidenceinobservingisdenotedasCostfounction:

（8）Friday,May12,202321第二十一页，编辑于星期六：二十三点二十二分。第21页，共25页。11、Netflixprizecomp