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时间:2019-02-28
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1、InformationSciences179(2009)3505–3519ContentslistsavailableatScienceDirectInformationSciencesjournalhomepage:www.elsevier.com/locate/insAhybridofsequentialrulesandcollaborativefilteringforproductrecommendationDuen-RenLiu*,Chin-HuiLai,Wang-JungLeeInstituteofInformationManagement,National
2、ChiaoTungUniversity,1001TaHseuhRoad,Hsinchu30050,TaiwanarticleinfoabstractArticlehistory:Customers’purchasebehaviormayvaryovertime.Traditionalcollaborativefiltering(CF)Received27June2007methodsmakerecommendationstoatargetcustomerbasedonthepurchasebehaviorReceivedinrevisedform13May2009of
3、customerswhosepreferencesaresimilartothoseofthetargetcustomer;however,Accepted5June2009themethodsdonotconsiderhowthecustomers’purchasebehaviormayvaryovertime.Incontrast,thesequentialrule-basedrecommendationmethodanalyzescustomers’pur-chasebehaviorovertimetoextractsequentialrulesinthefo
4、rm:purchasebehaviorinpre-viousperiods)purchasebehaviorinthecurrentperiod.Ifatargetcustomer’spurchaseKeywords:behaviorhistoryissimilartotheconditionalpartoftherule,thenhis/herpurchasebehav-CollaborativefilteringCustomersegmentationiorinthecurrentperiodisdeemedtobetheconsequentpartoftheru
5、le.AlthoughtheProductrecommendationsequentialrulemethodconsidersthesequenceofcustomers’purchasebehaviorovertime,Sequentialruleitdoesnotutilizethetargetcustomer’spurchasedataforthecurrentperiod.Toresolvetheaboveproblems,thisworkproposesanovelhybridrecommendationmethodthatcom-binestheseg
6、mentation-basedsequentialrulemethodwiththesegmentation-basedKNN-CFmethod.Theproposedmethodusescustomers’RFM(Recency,Frequency,andMonetary)valuestoclustercustomersintogroupswithsimilarRFMvalues.Foreachgroupofcustomers,sequentialrulesareextractedfromthepurchasesequencesofthatgrouptomaker
7、ecommendations.Meanwhile,thesegmentation-basedKNN-CFmethodprovidesrecommendationsbasedonthetargetcustomer’spurchasedataforthecurrentperiod.Then,theresultsofthetwomethodsarecombinedtomakefinalrecommendations.Exper-imentresultsshowthatthehybridmethodoutperformstraditionalCFmethods.Ó2009
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