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Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model

Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model

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时间：2019-07-12

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1、TransferLearningforCollaborativeFilteringviaaRating-MatrixGenerativeModelBinLilibin@fudan.edu.cnSchoolofComputerScience,FudanUniversity,Shanghai200433,ChinaQiangYangqyang@cse.ust.hkDept.ofComputerScience&Engineering,HongKongUniversityofScience&Technology

2、,HongKong,ChinaXiangyangXuexyxue@fudan.edu.cnSchoolofComputerScience,FudanUniversity,Shanghai200433,ChinaAbstractitemsbasedonacollectionoflike-mindedusers’ratingrecordsonthesamesetofitems.VariousCFmeth-Cross-domaincollaborativefilteringsolvesodshavebeenpr

3、oposedinthelastdecade.Forex-thesparsityproblembytransferringratingample,memory-basedmethods(Resnicketal.,1994;knowledgeacrossmultipledomains.InthisSarwaretal.,2001)findK-nearestneighborsbasedonpaper,weproposearating-matrixgenerativesomesimilaritymeasure.M

4、odel-basedmethods(Hof-model(RMGM)foreffectivecross-domainmann&Puzicha,1999;Pennocketal.,2000;Si&Jin,collaborativefiltering.Wefirstshowthat2003)learnprference/ratingmodelsforsimilaruserstherelatednessacrossmultipleratingmatri-(anditems).Matrixfactorizationme

5、thods(Srebro&cescanbeestablishedbyfindingasharedJaakkola,2003)findalow-rankapproximationfortheimplicitcluster-levelratingmatrix,whichisratingmatrix.Mostofthesemethodsarebasedonthenextextendedtoacluster-levelratingmodel.availableratingsinthegivenratingmatri

6、x.Thus,theConsequently,aratingmatrixofanyrelatedperformanceofthesemethodslargelydependsonthetaskcanbeviewedasdrawingasetofusersdensityofthegivenratingmatrix.anditemsfromauser-itemjointmixturemodelaswellasdrawingthecorrespondingHowever,inreal-worldrecomme

7、ndersystems,usersratingsfromthecluster-levelratingmodel.canrateaverylimitednumberofitems.Thus,theThecombinationofthesetwomodelsgivesratingmatrixisoftenextremelysparse.Asaresult,theRMGM,whichcanbeusedtofillthetheavailableratingdatathatcanbeusedforK-NNmissi

8、ngratingsforbothexistingandnewsearch,probabilisticmodeling,ormatrixfactorizationusers.AmajoradvantageofRMGMisthatareradicallyinsufficient.Thesparsityproblemhasitcansharetheknowledgebypoolingtherat-becomeamajorbottleneckformo

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