文章翻译 1.docx
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文章翻译 1.docx
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文章翻译1
ResearchonClusteringmethodBasedonAccessbehaviorofCustomerRequirements
BingLEI
SchoolofManagement,HenanUniversityofTechnology,Zhengzhou450001,China
Abstract:
Thispaperisbasedoncorporatewebsitevisiteddataandputforwardtheconceptofutilityofcustomerrequirementsalsobuildadatamodel.Onthisbasis,asimilarpreferenceofcustomersbasedoncustomerrequirementsclusteringmethodsisproposed.Thismethodincludescustomerrequirementutility(datamodeling),clusterofcustomerrequirement(clusterofvisitors),howtoconcludecharacteristicsofcustomersincluster(introductionofVisitorscharacteristics).Amongthem,fordatamodeling,Adesignbasedonthe‘visitor-customerdemand’matrixofcustomerdemandutilitydatamodelismadeandputsforwardtwostepsconstructionmethodofconstructing.
Keyword:
accessbehavior、customerrequirement、datamodeling、cluster、corporatewebsite
1Foreword
Basedontheenterprisewebsiteoperation,resultinginalargenumberofaccesstorecords,Theseaccessrecordsareleftbythecustomersduringtheyvisitthesites,Thecustomer's‘footprint’containstheiraccessintent.Ingeneral,theenterprisewebsitecustomeraccessrecordsaremainlyclickstreamandproductreviewstwocategories.BasedontheComprehensiveinformationtheory[1]andconsumerinformationprocessinganddecisiontheory[2],basedontheseclickstreamandproductreviewswecaninferthatthecustomerdemandforpreference.Therefore,Basedontheinferredpreferenceofcustomerdemand,Customersareclassified,thiswillbepropitioustoenterprisetomakeacorrespondingmarketingplanningorproduceproductwhichcanmeetdifferentcustomerdemand.
BasedonWebsiteaccessrecordstoresearchthebehaviorofonlineconsumer,belongtothefieldofintelligentbusinessattheapplicationlevel,atthetechnicallevelintheminingareaofwebusage.ResearchonapplicationofWebusageminingintermsofconsumerbehaviormainlyconcentratedinthepersonalizedproductrecommendationsystemandknowledgeofbusinessinformationminingtwoterms.
Intheaspectofpersonalizedproductrecommendationsystem,mainlybasedonthebrosehistoryofproductonthewebsites,onthisbasistorecommendproductstocustomers.Atpresent,thistechnologyisrelativelymature,andhasappliedtoeachbige-commercesites,suchasA,etc.Fromtheperspectiveoftechnology,personalizedrecommendationtechnologymainlyincludesthecollaborativefilteringandcontent-basedrecommendationtwocategories.Intheaspectofthecollaborativefiltering,mainlyincludesmemory-basedrecommendation[3]andmodel-basedrecommendation[4]twoalgorithms.Intheaspectofcontent-based,thekeypointisthataccesstoinformation[5]andinformationfiltering[6],namelybyanalyzingtheintroductionofproductstorecommend.
Intheaspectofknowledgeofbusinessinformationmining,Buehner,etc.[7]discussedTheminingmethodofcommercialwebsiteknowledgeindetail,Bythismethod,miningattractcustomersFromtheWebdata,customerretention,crosssalesandotherbusinessintelligencerulescanbedone.;Komati[8],etc.discussedthemethodofanalysisofthelessonslearnedfromtheB2Cwebsites.Fromthemanagementperspectiveintheliterature[9]Confirmsthefactorsthataffectcustomertosearchforinformationinthenetwork;Literature[10]studiesthekeytechnologyofIntelligentBusinessSystemsbasedonWebusagemining;Literature[11]putsforwardanintelligente-commercemodel,onthisbasis,akindofcustomerPurchasingbehaviorExtractionalgorithmandakindofMulti-objectiveoptimizationmodelareproposed
Allroundtheexistingnetworkconsumerbehaviorresearch,Mainlyincludesconsumersearchbehavior,Commodity-relatedrules,Personalizedrecommendation,etc.Fewresearchonapplicationofenterprisewebsiteaccessrecordsascustomerdemandofthedatasource.
ThisarticleisbasedonenterpriseWebsitevisiteddataofaccessbehaviorofvisitorsandanalysistheutilityofcustomerdemand.Onthisbasis,asimilarpreferenceofcustomersbasedoncustomerneedsclusteringmethodsisproposed.Themethodmainlysolveshowtoconfirmcustomerdemandutilityaccordingtoenterprisewebsiteaccessbehavior,Customerdemandclustering,howtoconcludeinclustersofcharacteristicsofcustomersandotherissues.
2.Customerdemandclusteringmethod
Basedontheinformationtheory[1],accesstocustomerneedsfromcustomerrecords,essentiallyfromthegrammartopragmaticslayer"progressive",namelyfromthesyntaxlayerof"clickstream"andthecommenttext,tothetermcustomerdemandatthesemanticlevel,andthentothecustomersdemandcharacteristicsofpragmaticlayertransformation.Generallyspeaking,everyvisitortovisitthewebsite(1ormore),producetheclickstreamandproductreviewdata,whichcontainsa"customerneeds"utility,therefore,toextractitfromthecustomerdemandsandutilityfunction,andthenestablish"visitors-customerdemandmatrix",onthisbasis,bythecustomerclustering,finallyhavethesame(orsimilar)needsofcustomers.
Figure1issuitableforenterprisecustomersclusteringmethodbasedonwebsiteaccessbehaviorframework.
Figure1customerneedsclusteringmethodbasedontheaccessbehaviorframework
Thebasicideaofthismethodisthatfirstofall,throughtheWebserverlogs,anddatabaseaccessto"clickstream"ofbusinessandthecommenttextdata,anddatamodeling.
Processdatamodelingisactuallyavisitoraccessrecordsfromthegrammartopragmaticslayer(customerdemandperspective)transformation.Inthetwosteps,oneisfromthesyntaxleveltosemanticlevel,twofromthesemanticlayertopragmaticlayer.
Theexpressionofsemanticlayer,thisarticleadoptsthewayofcustomerdemandcharacteristicsofkeywords.Herearetwokindsofcircumstances,forvisitorsofpageviews,willcombinewebsitecolumnsstructureextractiontopickeywordsonthepage;Fortheproductthecommenttext,willcommentonproductkeywordsextractedfeatures.Byextracting,itwillbebasedoncustomerdemandcharacteristicsofkeywordsemanticinformation.
Semanticlayer,however,getthekeywordswhilecanreflectthecharacteristicsoftheneedsofcustomers,butcannotexpresstheweightstothedifferentneedsofcustomers,alsocan'tclickontheflowandthecommenttextsemanticexpression,therefore,inpragmaticlevel,oneistheneedtoconstructtheutilityfunctiontofurtherclearcustomerdemand,thesecondistocombinetwoaccessrecordsofcustomerdemand,theresulting"visitors-customerrequirementsmatrix".
Datamodelingiscompleted,accordingtotherequirementof"customerclusteringvisitors-customerdemandmatrix",andsummarizethedemographiccharacteristicsofsimilardon'tcustomers,withthefinaldecisionsupportforenterprises.
3.Datamodeling
Needfordatapreprocessingbeforedatamodeling.Datapreprocessingincludingsurveyedpagerecognitionandcustomerrecognition,sessionidentificationandpathcompletion,customerdemandofkeyextract,etc.DatapreprocessingresultsgotacontainIinterviewedpage,jacustomerdemandofkeysetUC"visitors-surveyedcontent":
UC={UC1,UC2,…,UCk},
Thereinto,
UCk=(UIDk,PVk,RKk),KIdentifieroftheUIDkforvisitors.PVkkpageforvisitorsaccesstothecollection.RKkcustomerneedsinproductreviewsforvisitorsofkeyset.
PVk={(Pk1,Tk1),(Pk2,Tk2),…,(PkI,Tki)},PkIkforvisitorstoaccessthepageURL,TkIkforvisitorstoaccesstheIthelengthofthepage.
RKk={(Kk1,Nk1),(Kk2,Nk2)…,(Kkj,Nkj)},AKKJKmentionedintheproductreviewsforvisitorstothejfeature,NK1asthenumberofvisitorstotheKJfeaturewords.
Accordingtothefigure1shows,Datamodelingisthekeypointofthismethod.Theessenceofwhichistobuildaccessbehaviorofthecustomerneedstheutilityfunction.Currently,aboutaccessbehaviorbasedutilityfunctionofthedemandintheproductresearchareveryfew,butalsoaresearchdifficulties.Therefore,asanexploratorystudy,thispaperpresentsamethodofbuildingutilityfunctionbasedoncustomerdemandcharacteristicsofkeywords,namelydatamodelingmethod.
Thebasicideaofthismethodistosimplifytheaccessbehaviorofcustomer’sneedsutilityforabinaryset,namely(keywords,weight).ThisexpressionisalsotypicalbusinessdatamodelingmethodinWebusemining[12].
Isproposedinthispapertobuild"visitors-customerrequirements"matrixtorepresenttheneedsofcustomersutilitydatamodelingmethod,thedatamodelingprocessisdividedintotwosteps.AwasestablishedaccordingtotheUCvisitors-pagequestioned"thematrix,"visitors-key"andsecond,onthisbasis,thecombinationofcustomerdemandknowledgebase,buildcontentenhancedmatrix--"visitors-customerrequirements"matrix.
3.1"visitors-page"questionedthematrix.
Definition1"Visitors-pagequestionedmatrix"referstothevisitorsasaline,websitealltheneedanalysisofthepageascolumn,valuesastheaccesstime(withoutaccesstothevalue0)matrix.
AssumesthatthewebsitealltheneedanalysisofthesetofpagesforP,:
P={P(1,2,P...,Pm},andvisitorssetkofthepage,asaresult,visitthelengthineachrowinthedata,therearem-(I)avalueof0.
Indatapretreatmentphase,inordertopathcompletion,oftenaccordingtothefirstSession,Sessionidentificationissetbutavisitornumberofsessionsoveraperiodoftimetendtobemorethanone,andeachSessionofthesurveyedpagesmayrepeatvisit.Therefore,whenvisitorstogenerateksetofpage,needtomergethesamepage,itsaccesstimebyaccumula
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