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4xccccc

∙AnOptimizedTrustFactorBasedCollaborativeFilteringRecommendationAlgorithminE-commerce

XinyiBu;XiujuanHe;

InformationScienceandManagementEngineering（ISME）,2010InternationalConferenceof

Volume:

1

DigitalObjectIdentifier:

10.1109/ISME.2010.157

PublicationYear:

2010,Page（s）:

468-471

IEEEConferences

Abstract | FullText:

PDF （255KB）

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

∙

ANeuralNetworks-BasedClusteringCollaborativeFilteringAlgorithminE-CommerceRecommendationSystem

JianyingMai;YongjianFan;YanguangShen;

WebInformationSystemsandMining,2009.WISM2009.InternationalConferenceon

DigitalObjectIdentifier:

10.1109/WISM.2009.129

PublicationYear:

2009,Page（s）:

616-619

IEEEConferences

Abstract | FullText:

PDF （276KB）

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

∙

Usingcategory-basedcollaborativefilteringintheActiveWebMuseum

Kohrs,A.;Merialdo,B.;

MultimediaandExpo,2000.ICME2000.2000IEEEInternationalConferenceon

Volume:

1

DigitalObjectIdentifier:

10.1109/ICME.2000.869613

PublicationYear:

2000,Page（s）:

351-354vol.1

IEEEConferences

Abstract | FullText:

PDF （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

DigitalObjectIdentifier:

10.1109/IADCC.2009.4809245

PublicationYear:

2009,Page（s）:

1526-1529

IEEEConferences

Abstract | FullText:

PDF （2491KB）

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

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MemeticCollaborativeFilteringBasedRecommenderSystem

Banati,H.;Mehta,S.;

InformationTechnologyforRealWorldProblems（VCON）,2010SecondVaagdeviInternationalConferenceon

DigitalObjectIdentifier:

10.1109/VCON.2010.28

PublicationYear:

2010,Page（s）:

102-107

IEEEConferences

Abstract | FullText:

PDF （296KB）

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

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SyncretizingContextInformationintotheCollaborativeFilteringRecommendation

RuliangXiao;FaliangHong;JinboXiong;XiaojianZheng;ZhengqiuZhang;

DatabaseTechnologyandApplications,2009FirstInternationalWorkshopon

DigitalObjectIdentifier:

10.1109/DBTA.2009.57

PublicationYear:

2009,Page（s）:

33-36

IEEEConferences

Abstract | FullText:

PDF （357KB）

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

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AddressingInterestDiversityinP2PBasedCollaborativeSpamFiltering

FangWeidong;DongShoubin;

GridandCooperativeComputingWorkshops,2006.GCCW'06.FifthInternationalConferenceon

DigitalObjectIdentifier:

10.1109/GCCW.2006.16

PublicationYear:

2006,Page（s）:

163-169

IEEEConferences

Abstract | FullText:

PDF （271KB）

CollaborativeinformationfilteringtendstobeapromisingtechnologyinthefightagainInternetspam,andthepeer-to-peerframeworkisbelievedtobemoresuitabletoimplementthiscollaborationcomparedtocentralizedones.However,theassumptionofuniforminformationinterestsacrosspeersincollaborativefilteringlimitstheimprovementinfilteringaccuracyandincreasesnetworktrafficunnecessarily.Toaddressthisproblem,weproposetoconstructmultipleinterestgroupsforapeer,eachcorrespondingtoonemessagecategory;andtouseapercolationsearchalgorithmtoretrievemessagefeedbacksinscale-freeemailnetworks.ExperimentsshowtheproposedmodelcangetremarkableimprovementinfalsepositivereductionandgoodperformanceinspamfeedbackretrievalReadMore»

∙

LearningtheSpectrumviaCollaborativeFilteringinCognitiveRadioNetworks

HushengLi;

NewFrontiersinDynamicSpectrum,2010IEEESymposiumon

DigitalObjectIdentifier:

10.1109/DYSPAN.2010.5457847

PublicationYear:

2010,Page（s）:

1-12

IEEEConferences

Abstract | FullText:

PDF （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

DigitalObjectIdentifier:

10.1109/SERA.2007.33

PublicationYear:

2007,Page（s）:

297-304

IEEEConferences

Abstract | FullText:

PDF （314KB）

Thepurposeofthisstudyistosuggestanal