4xccccc文档格式.docx

4xccccc文档格式.docx

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10.1109/ISME.2010.157

PublicationYear:

2010,Page（s）:

468-471

IEEEConferences

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Typicaltrustfactorbasedcollaborativefilteringalgorithmisnotsuitableforuser'

smultipleinterestrecommendation.Anewalgorithmcombinestraditionaltrustfactor-basedcollaborativefilteringwithsimilaritem-basedcollaborativefilteringwaspresented.Theexperimentalresultsshowthattheproposedmethodsperformbetterthantheoldrecommendationmethod.ReadMore»

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ANeuralNetworks-BasedClusteringCollaborativeFilteringAlgorithminE-CommerceRecommendationSystem

JianyingMai;

YongjianFan;

YanguangShen;

WebInformationSystemsandMining,2009.WISM2009.InternationalConferenceon

10.1109/WISM.2009.129

2009,Page（s）:

616-619

（276KB）

E-commercerecommendationsystemisoneofthemostimportantandthemostsuccessfulapplicationfieldofdataminingtechnology.Recommendationalgorithmisthecoreoftherecommendationsystem.Inthispaper,aneuralnetworks-basedclusteringcollaborativefilteringalgorithmine-commercerecommendationsystemisdesigned,tryingtoestablishanclassifiermodelbasedonBPneuralnetworkforthepre-classificationtoitemsandgivingrealizationofclusteringcollaborativefilteringalgorithmandBPneuralnetworkalgorithm,andcarryingontheanalysisanddiscussiontothisalgorithmfrommultipleaspects.Thisalgorithmishelpfultoimprovesparsityproblemofcollaborativefilteringalgorithmandtoformthemoreeffectiveandthemoreaccuraterecommendationresult.ReadMore»

Usingcategory-basedcollaborativefilteringintheActiveWebMuseum

Kohrs,A.;

Merialdo,B.;

MultimediaandExpo,2000.ICME2000.2000IEEEInternationalConferenceon

10.1109/ICME.2000.869613

2000,Page（s）:

351-354vol.1

（488KB）

Collaborativefilteringisanimportanttechnologyforcreatinguser-adaptingWebsites.Ingeneraltheeffortsofimprovingfilteringalgorithmsandusingthepredictionsforthepresentationoffilteredobjectsaredecoupled.Therefore,commonmeasures（ormetrics）forevaluatingcollaborativefiltering（recommender）systemsfocusmainlyonthepredictionalgorithm.Itishardtorelatetheclassicmeasurementstoactualusersatisfactionbecauseofthewaytheuserinteractswiththerecommendations,determinedbytheirrepresentation,influencesthebenefitsfortheuser.Weproposeanabstractaccessparadigm,whichcanbeappliedtothedesignoffilteringsystems,andatthesametimeformalizestheaccesstofilteringresultsviamulti-corridors（basedoncontent-basedcategories）.Thisleadstonewmeasureswhichbetterrelatetotheusersatisfaction.Weusethesemeasurestoevaluatetheuseofvariouskindsofmulti-corridorsforourprototypeuser-adaptingWebsite,theActiveWebMuseumReadMore»

AScalableCollaborativeFilteringBasedRecommenderSystemUsingIncrementalClustering

Chakraborty,P.S.;

AdvanceComputingConference,2009.IACC2009.IEEEInternational

10.1109/IADCC.2009.4809245

1526-1529

（2491KB）

RecommendersystemshelptoovercometheproblemofinformationoverloadontheInternetbyprovidingpersonalizedrecommendationstotheusers.Content-basedfilteringandcollaborativefilteringareusuallyappliedtopredicttheserecommendations.Amongthesetwo,Collaborativefilteringisthemostcommonapproachfordesigninge-commercerecommendersystems.TwomajorchallengesforCFbasedrecommendersystemsarescalabilityandsparsity.Inthispaperwepresentanincrementalclusteringapproachtoimprovethescalabilityofcollaborativefiltering.ReadMore»

MemeticCollaborativeFilteringBasedRecommenderSystem

Banati,H.;

Mehta,S.;

InformationTechnologyforRealWorldProblems（VCON）,2010SecondVaagdeviInternationalConferenceon

10.1109/VCON.2010.28

102-107

（296KB）

WebbasedDecisionSupportsystemslikerecommendationsystemshavebecomeeffectivetoolsfordecisionmakingintherecentpast.Howevertherecommendersystemsemployingconventionalclusteringtechniques（KRS）likeK-Meansforcollaborativefiltering,sufferfromthelimitationofgettinglocaloptimumresults.ThispaperpresentsMemeticRecommenderSystem（MRS）basedonthecollaborativebehaviorofmemes.MemeticAlgorithms（MAs）areconsideredasoneofthemostsuccessfulapproachesforcombinatorialoptimization.MAsarethegeneticalgorithmswhichincorporatelocalsearchintheevolutionaryscheme.Weproposeadistinctivestrategytoperformlocalsearchinmemeticalgorithms.MRSworksin2phases-InthefirstphaseamodelisdevelopedbasedonMemeticClusteringalgorithmandinthesecondphasetrainedmodelisusedtopredictrecommendationsfortheactiveuser.RigorousexperimentswereconductedtoprovethedecisionsupportandstatisticalefficacyofMRSvisavisKRS.Resultsconfirmedthattheproposedapproachyieldsmuchbetterperformanceascomparedtotheconventionalcollaborativefilteringrecommendersystem.ReadMore»

SyncretizingContextInformationintotheCollaborativeFilteringRecommendation

RuliangXiao;

FaliangHong;

JinboXiong;

XiaojianZheng;

ZhengqiuZhang;

DatabaseTechnologyandApplications,2009FirstInternationalWorkshopon

10.1109/DBTA.2009.57

33-36

（357KB）

Socialnetworkallowsuserstoorganizecollectionsofresourcesonthewebinacollaborativefashion.Collaborativefilteringasaclassicalmethodhasbeenalsousedinhelpingpeopletodealwithinformationoverloadinfolksonomysystem.Theproblemofdevisingmethodstosolvethecontextualproblemsemergingintheprocessofrecommendationapplicationoverthesocialnetworkisincreasingopen.Hereweproposeanovelmeanstosyncretizecontextinformationintotherecommendersystem.Thispaperfirstrecalltraditionalmethodsofcollaborativefiltering,thenpresentssomedefinitionsandalgorithmframework,proposesacontextualratingestimation.Finally,experimentcomparisondemonstratesthatthecontextualapproachcanproducesbetterratingestimations.ReadMore»

AddressingInterestDiversityinP2PBasedCollaborativeSpamFiltering

FangWeidong;

DongShoubin;

GridandCooperativeComputingWorkshops,2006.GCCW'

06.FifthInternationalConferenceon

10.1109/GCCW.2006.16

2006,Page（s）:

163-169

（271KB）

CollaborativeinformationfilteringtendstobeapromisingtechnologyinthefightagainInternetspam,andthepeer-to-peerframeworkisbelievedtobemoresuitabletoimplementthiscollaborationcomparedtocentralizedones.However,theassumptionofuniforminformationinterestsacrosspeersincollaborativefilteringlimitstheimprovementinfilteringaccuracyandincreasesnetworktrafficunnecessarily.Toaddressthisproblem,weproposetoconstructmultipleinterestgroupsforapeer,eachcorrespondingtoonemessagecategory;

andtouseapercolationsearchalgorithmtoretrievemessagefeedbacksinscale-freeemailnetworks.ExperimentsshowtheproposedmodelcangetremarkableimprovementinfalsepositivereductionandgoodperformanceinspamfeedbackretrievalReadMore»

LearningtheSpectrumviaCollaborativeFilteringinCognitiveRadioNetworks

HushengLi;

NewFrontiersinDynamicSpectrum,2010IEEESymposiumon

10.1109/DYSPAN.2010.5457847

1-12

（336KB）

Secondaryusersincognitiveradionetworksneedtolearnthestatisticsofspectruminordertoachieveefficientcommunications.Duetothespatialcorrelation,theefficiencyoflearningisimprovedbylettingsecondaryuserscollaborateandexchangeinformation.DuetothesimilaritybetweenthecollaborativelearningincognitiveradionetworksandtherecommendationsystemsofelectroniccommercelikeAmazon,thetechniqueofcollaborativefilteringisapplied.Predictionorientedandrewardorientedcriteriaareproposedtoderivetheprocedureofcollaborativefiltering.Fortheformercriterion,linearpredictionisusedfortheparameterestimation,heuristicmetricisderivedforchannelselection,andsimilaritybasedBoltzmandistributionisusedforcollaboratorselection.Forthelattercriterion,thetechniqueofmulti-armedbanditisappliedtomaximizethetotalrewardofspectrumaccess.Numericalsimulationshowsthattheproposedcollaborativefilteringschemecansignificantlyimprovetheperformanceofspectrumlearning.ReadMore»

AStudyontheImprovedCollaborativeFilteringAlgorithmforRecommenderSystem

HeeChoonLee;

SeokJunLee;

YoungJunChung;

SoftwareEngineeringResearch,Management&

Applications,2007.SERA2007.5thACISInternationalConferenceon

10.1109/SERA.2007.33

2007,Page（s）:

297-304

（314KB）

Thepurposeofthisstudyistosuggestanal