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1、ABSTRACTABSTRACTWiththecombinationofcloudcomputingandeducationinformatization,educationcloudplatformisfavoredbymoreandmoreeducationinstitutionsandusersforitsconvenientResourcesharing,efficientcentralizedmanagement,wideareacoverageandtheadvantagesoftheeducationre
2、sourcesinawiderangeofhighquality.Thisleadtotherapidgrowthofcoursewareresourcesstoredineducationcloudplatforms.Vastamountsofcoursewareresourcesalsobringoutaproblemofinformationoverloadwhenusersstudyintheplatform.itishardtofindthelearningmaterialstheyareinterested
3、withonlythetraditionalclassificationsearchandkeywordretrievalmethod.Forthis,personalizedpushserviceappeared.Nowthepersonalizedrecommendationserviceprovidedforusersismostlybasedontheirevaluationinformationforcertainitems,iftheusershasnoevaluationinformation,thesy
4、stemwillnotbeabletomakerecommendationsforthem,andnowmostofthepopularrecommendationalgorithmsarebasedonthebasicofstand-alonemode,thecomputingcapacityandsystemscalabilityhasgreatlimitations,andtheycan'treallyadapttothemassivedataanalysisandprocessingofthecloudplat
5、form.Therefore,thisthesisdesignsandimplementsapersonalizedcoursewarerecommendationsystembasedoneducationcloudplatform.Thisthesisaimsatthecoursewarerecommendationservicefortheeducationcloudplatformusers.Firstly,itsummarizesthestatusquooftheresearchesintheeducatio
6、ncloudplatformandrecommendationtechnology.Then,itgivesadetailedin-depthresearchonthebuildprocessoftherecommendedsystemandthedesignprocessbasedonMapReduceprogram.Accordingtotheactualsituationofthisissue,thisthesiscarriesonademandanalysisforthesystemfromtheusers'i
7、nterestmodel,coursewaremodel,recommendationmodelandthehigh-performancemassdataprocessing,proposesaoverallarchitectureofthesystem,givesadetaileddesignontheinterestmodeloftheusersandtheobjectmodelofthecoursewhichareconstructedbytheMapReducecomputationmodel,andfini
8、shedthelatentfactorcourserecommendationmodelbasedontopic,thecourserecommendationmodelbasedonthecontentandthepopularity.Finally,thisthesisteststhesystemfromfunctiontop
