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1、InformationSystems49(2015)182–199ContentslistsavailableatScienceDirectInformationSystemsjournalhomepage:www.elsevier.com/locate/infosys$Contextualpreferenceminingforuserprofileconstructionab,ccSandradeAmo,MouhamadouSaliouDiallo,CheikhTalibouyaDiop,bbbArnaudGiacometti,DominiqueLi,A
2、rnaudSouletaUniversidadeFederaldeUberlândia,Av.JoãoNavesdeAvila212138400-902Uberlândia,MG,BrazilbUniversitéFrançoisRabelaisdeTours,3PlaceJeanJaurès41029Blois,FrancecUniversitéGastonBerger,BP234,Saint-Louis,SenegalarticleinfoabstractArticlehistory:Theemergingofubiquitouscomputingte
3、chnologiesinrecentyearshasgivenrisetoaReceived5February2014newfieldofresearchconsistinginincorporatingcontext-awarepreferencequeryingReceivedinrevisedformfacilitiesindatabasesystems.OneimportantstepinthissettingisthePreferenceElicitation13November2014taskwhichconsistsinprovidingth
4、euserwaystoinformhis/herchoiceonpairsofobjectsAccepted22November2014withaminimaleffort.InthispaperweproposeanautomaticpreferenceelicitationRecommendedby:F.KornAvailableonline3December2014methodbasedonminingtechniques.Themethodconsistsinextractingauserprofilefromasetofuserpreferenc
5、esamples.Inoursetting,aprofileisspecifiedbyasetofcontextualKeywords:preferencerulesverifyingpropertiesofsoundnessandconciseness.AfterprovingthatthePatternminingproblemisNP-complete,weproposearesolutionin2phases.ThefirstphaseextractsallPreferenceelicitationindividualuserpreferences
6、bymeansofcontextualpreferencerules.ThesecondphaseContextualpreferencerulebuildstheuserprofilestartingfromthiscollectionofrulesusingagreedymethod.ToUserprofileminingassessthequalityofuserprofiles,weproposethreerankingtechniquesbenefitingfromtheseprofilesthatenableustorankobjectsacc
7、ordingtouserpreferences.Weevaluatetheefficacyofourthreerankingstrategiesandcomparethemwithawell-knownrankingmethod(SVMRANK).Theevaluationiscarriedoutthroughanextensivesetofexperimentsexecutedonareal-worlddatabaseofuserpreferencesaboutmovies.&2014ElsevierLtd.Allrightsreserved.1.Int
8、roductionessentialinthesystemmode
