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1、FlexibleMixtureModelforCollaborativeFilteringLuoSiLSI@CS.CMU.EDURongJinRONG@CS.CMU.EDUSchoolofComputerScience,CarnegieMellonUniversity,Pittsburgh,PA15232USAAbstractusersinthetrainingdatabasesimilartothetestuserandthen,predictthetestuser’sratingsbasedontheThispaperpresen
2、tsaflexiblemixturemodelcorrespondingratingsofthesesimilarusers.Onthe(FMM)forcollaborativefiltering.FMMextendscontrary,model-basedalgorithmsbuildmodelsthatareexistingpartitioning/clusteringalgorithmsforabletoexplainthetrainingexampleswellandpredictthecollaborativefilteri
3、ngbyclusteringbothusersratingsoftestusersusingtheestimatedmodels.Bothofanditemstogethersimultaneouslywithoutthememory-basedalgorithmsandthemodel-basedassumingthateachuseranditemshouldonlyalgorithmshavetheiradvantagesanddisadvantages.belongtoasinglecluster.Furthermore,wi
4、ththeMemory-basedalgorithmshavemuchlessoff-lineintroductionof‘preference’nodes,theproposedcomputationcostswhilethemodel-basedalgorithmsmayframeworkisabletoexplicitlymodelhowusershavelesson-linecomputationcosts.rateitems,whichcanvarydramatically,evenamongtheuserswithsimi
5、lartastesonitems.Thoughmemory-basedandmodel-basedapproachesEmpiricalstudyovertwodatasetsofmoviedifferfromeachotherinmanyaspects,bothofthemratingshasshownthatournewalgorithmassumethatuserswithsimilartastesshouldrateitemsoutperformsfiveothercollaborativefilteringsimilarly
6、andthereforetheideaofclusteringisusedinalgorithmssubstantially.bothapproacheseitherexplicitlyorimplicitly.Formemory-basedapproaches,traininguserssimilartothetestuseraregroupedtogetherandtheirratingsare1.Introductioncombinedtopredictratingsforthetestuser.Meanwhile,model-
7、basedapproachesclusteritemsand/ortrainingTherapidgrowthoftheinformationontheInternetusersintoclassesexplicitlyandpredictratingsofatestdemandsintelligentinformationagentthatcansiftuserbysimplyusingtheratingsofclassesthatfitinbestthroughalltheavailableinformationandfindou
8、tthewiththetestuserand/oritemstoberated.Thus,howtomostvaluabletous.Theseintelligentsystemscanbeclusterusersand
