文章翻译 1文档格式.docx
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文章翻译 1文档格式.docx
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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"
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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