In57357uenc is measure of the e57355ect of user on the recommendations from recommender system In 57357uence is erful to ol for understanding the orkings of recommender system Exp erimen ts sho that users ha widely arying degrees of in57357uence in ID: 26939 Download Pdf
Dietmar. . Jannach. , Markus . Zanker. , Alexander . Felfernig. , Gerhard Friedrich. Cambridge University Press. Which digital camera should I buy. ?. What is the best holiday for me and. my family.
umnedu GroupLens Research Group Army HPC Research Center Department of Computer Science and Engineering University of Minnesota Minneapolis MN 55455 USA Abstract We investigate the use of dimensionality reduction to improve the performance for a new
Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design.
Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design.
Evaluating Recommender Systems. A myriad of techniques has been proposed, . but. Which one is the best in a given application domain?. What are the success factors of different techniques?. Comparative analysis based on an optimality criterion? .
Bamshad Mobasher. DePaul University. 2. What Is Prediction?. Prediction is similar to classification. First, construct a model. Second, use model to predict unknown value. Prediction is different from classification.
e-Commerce and Life Style Informatics: . Recommender Systems I. February 4 2013. Geoffrey Fox. gcf@indiana.edu. . . http://. www.infomall.org/X-InformaticsSpring2013/index.html. . Associate Dean for Research and Graduate Studies, School of Informatics and Computing.
Dr. Frank McCown. Intro to Web Science. Harding University. This work is licensed under Creative . Commons . Attribution-. NonCommercial. . 3.0. Image: . http://lifehacker.com/5642050/five-best-movie-recommendation-services.
in the Presence of Adversaries?. Bamshad Mobasher. Center for Web Intelligence. School of Computing, DePaul University, Chicago, Illinois, USA. Personalization / Recommendation Problem. Dynamically serve customized content (pages, products, recommendations, etc.) to users based on their profiles, preferences, or expected interests.
Rachel Pottinger. University of British Columbia. Joint work with lots of great students, including Zainab Zolaktaf, Reza Babanezhad, . Jian Xu, Omar AlOmeir. , and . Janik. Andreas. Exploring and understanding data.
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In57357uenc is measure of the e57355ect of user on the recommendations from recommender system In 57357uence is erful to ol for understanding the orkings of recommender system Exp erimen ts sho that users ha widely arying degrees of in57357uence in
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In\ruenceinRatings-BasedRecommenderSystems:AnAlgorithm-IndependentApproachAlMamunurRashidGeorgeKarypisJohnRiedlAbstractsystemshavebeenshowntohelpusersnditemsofinterestfromamongalargepoolofpotentiallyin-terestingitems.In\ruenceisameasureoftheeectofauserontherecommendationsfromarecommendersystem.In-\ruenceisapowerfultoolforunderstandingtheworkingsofarecommendersystem.Experimentsshowthatusershavewidelyvaryingdegreesofin\ruenceinratings-basedrecom-mendersystems.Proposedin\ruencemeasureshavebeenalgorithm-specic,whichlimitstheirgeneralityandcompa-rability.Weproposeanalgorithm-independentdenitionofin\ruencethatcanbeappliedtoanyratings-basedrecom-mendersystem.Weshowexperimentallythatin\ruencemaybeeectivelyestimatedusingsimple,inexpensivemetrics.1IntroductionSociologistshavelongtriedtocharacterizethein\ruenceofapersoninasocialnetworkofmanypeople[1].Iden-tifyingthein\ruentialpeoplecanbringtwinadvantagestothosewhostudygroupdynamics:(1)Thein\ruen-tialpeoplecanbedirectlystudied,yieldinginsightsincetheirchoicesmaybepredictiveofgroupchoices;or(2)Thein\ruentialpeoplemaybein\ruencedtochangethebehaviorofthegroup.Manysocialnetworksareformedandmaintainedthroughinformal,qualitative,andun-observedinteractions.Capturingdataaboutthesein-teractionsisdicult,andtheactofcapturingthosedatamaychangethesocialinteractionsthemselves.CollaborativeFiltering(CF)recommendersystems[2,3,4]basetheirdecisionsontheopinionsofusers.Incontrasttoothersocialnetworks,recommendersystemscaptureinteractionsthatareformal,quantitative,andobserved.Thesocialnetworkcanbeanalyzeddirectlythroughdataalreadycapturedinthecomputersystem.Pastresearchhasdemonstratedthatanalyzingthesocialnetworkcanprovideleverageinin\ruencingthegroup[5].Theanalysisperformedinthesestudiesisbasedonadeepinvestigationofthecharacteristicsofoneparticularrecommenderalgorithm,thewell-knownuser-usernearestneighboralgorithm[2].Carefulanalysisofthistypehasmanyadvantages,butonekeydisadvantage:itistiedcloselytothedetailsofthealgorithm.Inprinciple,similartechniquescouldDepartmentofComputerScience&Engineering,Univer-sityofMinnesota,Minneapolis,MN-55455,farashid,karypis,riedlg@cs.umn.edubeappliedtootheralgorithms,butdoingsowouldbelaborious,andtheresultingin\ruencemeasureonlyappliestoalgorithmsthatworkpreciselyaccordingtothedetailsoftheanalysis.Sincemanycommercialoperatorstweaktheoperationoftherecommenderinmanywaystottheneedsoftheirbusiness,thisanalysismaynotapplyinpractice.Further,theresultingmeasuresofin\ruencewouldbeunlikelytobecomparablebetweendierentalgorithms,sincetheyhavebeenproducedthroughverydierenttechniques.Akeygoalofthepresentresearchistoidentifyameasureofin\ruenceforrecommendersystemsthatisapplicabletoanyratings-basedrecommendersystem,independentoftheparticularsofthealgorithm.Suchameasurewouldallowforconsistent,black-boxanalysisofin\ruence.2RelatedWork2.1RecommenderSystems.Resnick,etal.[2]in-troducedanautomaticcollaborativelteringalgorithmbasedonak-nearestneighbors(kNN)algorithmamongusers;thisalgorithmisnowcalleduser-userCF.Theuser-useralgorithmweuseinthispaperisaversionoftheoriginalkNNalgorithm,tunedtoachievebestknownperformance.Sarwaretal.[4]proposedanal-ternativekNNCFalgorithmbasedonsimilarityamongitems.Thisvariantisoftencalleditem-itemCF.Breeseetal.[3]havedividedanumberofCFalgorithmsintotwoclasses:memory-basedalgorithmsandmodel-basedalgorithms.OvertheyearsmanyotheralgorithmswereproposedincludingonesbasedonSVD,cluster-ing,BayesianNetworks[3].Wefocusontheuser-useranditem-itemalgorithmsinthispaperbecausetheyarethemostcommoninexistingsystems.2.2SocialNetworksandIn\ruence.ASocialnet-workisaformofgraphdelineatingrelationshipsandinteractionsamongindividuals.Findingtheimportantnodesinsuchgraphshasbeenanobjectofinteresttosociologistsforalongtime.Oneproposedmeasureforimportanceiscentrality[1].Twoexamplesof\cen-trality"measuresare\degreecentrality",whichtreatshighdegreenodesasimportant,and\distancecentral- ui,muN2,muN1,muN3,muN4,muN5,muN6,muN7,muNk,mFigure1:Showingthenotionofin-linksforthekclosestneighborsofui.Here,predictionisbeingcomputedforthe(user,item)pair,(ui;m).ity",whichtreatsnodeswithshortpathstomanyothernodesasimportant[1].Kleinberg'sHITS[6],andBrinandPage'sPageRank[7]algorithmsfororderingnodesinagraphofwebarebasedonsocialnetworkprinciples.Domingosetal.[5]havestudiedtheproblemofchoosingin\ruentialusersformarketerswhowishtoattractattentiontotheirproducts.Theyshowthatselectingtherightsetofusersforamarketingcampaigncanmakeabigdierence.Kempeetal.[8]focusonacollectionofmodelswidelystudiedinsocialnetworks,aswellasthemodelsin[5],underthecategories:LinearThresholdModels,andIndependentCascadeModels.Ourresearchalsoinvestigatesin\ruenceinsocialnetworks.LikeDomingosetal.wefocusonnetworksinrecommendersystems.Weextendtheirresearchtogeneralmeasuresofin\ruencethatareindependentoftheparticularrecommenderalgorithmbeingused.3DeningIn\ruentialUsersinCFSystemsWerstdiscussthedatausedinthisproject,thenan-alyzeapopularCFalgorithmtounderstandapossibleformationprocessofin\ruentialusers,andthentrydif-ferentwaystosetthedenition.3.1TheData.Wehaveusedapubliclyavailabledatasetfromwww.grouplens.org.ThedatasetisafractionoftheusagedatadrawnfromMovieLens(www.movielens.org),aCF-basedonlinemovierec-ommendationsystem.Itcontains6,040users,3,593movies,andaboutonemillionratingsona5-starscale.Eachuserhasratedatleast20moviesinthedataset.Wehavepartitionedthisdataintotrainingandtestsetsbyarandom80%/20%split.3.2TheUser-UserAlgorithm.ThemostwidelycitedandarguablythemostcommonlyusedCFalgo-rithminresearchisakNN-basedalgorithm.Inthisschemetheusers'preferencedataisrepresentedinanmuser-itemmatrixforasystemwithnusersandmitems,wherethe(i;j)-thentryofthismatrixstandsfortheuserui'sratingonitemj,ornull,dependingonwhethertheuseruihasratedtheitemj,ornot,respectively.Theuser-useralgorithmcanbethoughtofworkingintwostages.Intherststage,similari-tiesbetweeneverypairofusersarecomputedandarestoredasamodel.Althoughmanydierentformula-tionsarepossibleforsimilarityweightcalculations,theGroupLens[2]proposedmechanismisthePearsoncor-relationcoecient.Accordingly,thesimilarityweightbetweentwousers,ui,andujismeasuredbyequation3.1:Wij=Pk2I(Rik Ri)(Rjk Rj)qPk2I(Rik Ri)2Pk2I(Rjk Rj)2(3.1)Iisthesetofitemsratedbybothoftheusers,Rikisuserui'sratingonitemk,andRiistheaverageratingofui.Usingthissimilaritymetric,thenextstep,predictiongeneration,iscarriedoutasfollows.Predictiononitemaforuseruiiscomputedbypickingknearestuserswhohavealsorateditema,andbyapplyingaweightedaverageofdeviationsfromtheselectedusers'means:Pia=Ri+Pk=1(Rua Ru)WiuPk=1Wiu(3.2)SomePlausibleIn\ruenceMetricsBasedonPriorWork.Wecannowproposeseveralin\ruencemetrics.Onetypeofmetricismotivatedbytargetedmarketing.Anothertypeofmetricexploitsconnectionsbetweenusersbasedonsimilarity.3.3.1ExpectedLiftinProt:NetworkValues.Thisapproach,asoutlinedin[5],isbasedonthegoaloftargetedmarketing.Inthisscheme,userswhocanyieldthemostexpectedliftinprotbymakingacascadingadoptionofaproducthappen,areconsideredasin\ruentialusers.Domingosetal.[5]haveappliedthisideaonarecommendationsystemdatasetbasedontheuser-userCFalgorithmdescribedinthelastsection.Theprobabilisticmodelin[5]isbasedontheMarkovRandomFields,whichrequirestheneighborsbesymmetric;i.e.,twousersareneighborstoeachotherifoneofthemisaneighbortotheother.TheauthorsmentionthatinakNN-basedCFsystem,thismightnothold.Again,ELPNetworkValueistiedtoaparticularproduct;morespecically,itisspecictoasetoffeaturesoftheproductbeingmarketed.TranslatingthisissueintotheRSdomain,ELPNetworkValues arespecictoparticulargenrevectors.Thusauser'sELPNetworkValuewilldierformovieswithdierentgenrevectors.3.3.2NetworkStructure:SimilarityLinks.Bycloselyobservingtheprocessofneighbor-selection,wenoticesomenetworkstructurethatcouldfacilitateinformingadenitionforin\ruentialusers.Figure1demonstratesasituationwherethesystemiscomputingapredictiononitemmforuserui.Inordertodoso,itselectstopkneighborswhoalsohaveratedtheitemm.Nowwecanimaginedirectededgesfromuitowardseachofthekneighbors.Equations3.3and3.4showtheupdatedauthorityandhubequations.Inordertoconsiderthefactthatallthelinksmaynotofsameweight,wehaveincorporatedaweighttermsimilarto[9]tothebasicHITS[6]equations.Heretheconditionalprobability,p(ijj)referstothedegreeofuseruj'spresenceindicatinguserui'spresence.a(i)=Xj!ip(ijj)h(j)Wij(3.3)h(i)=Xi!jp(jji)a(j)Wij(3.4)Wecanusethismodiedauthoritytorepresentin\ruence.Thedrawbackofthisschemeofin\ruence,however,isalgorithmdependence:thenetworkstructurecap-turedhereisverymuchalgorithm-specic;and,forotheralgorithms,thestructuremightnotbeasap-parent.Inordertoderiveadenitionthatisgenericenough,yetsimple,weusetheHide-one-Userapproachdiscussednext.Thefundamentalconceptwiththisap-proachisguringoutwhichusercausesthelargestcu-mulativechangeofpredictioninthesystem.4Algorithm-IndependentIn\ruenceThesemetricsdenein\ruenceastheamountofeectauserhasoverothersviathepredictionstheyreceive.Onewaytoobservethiseectistoexcludeauserandmeasurethenetchangesinpredictionscausedbytheremoval.Theidea:LetUbethesetofavailableusersinthesystem,MUbethemodelbuiltwiththepreferencedataofthissetofusers.WecallNPDui(NumberofPrediction-Dierences)asthenumberoftimesthefollowingexpressionholdstrue:jPja(MU) Pja(MU fUig)j;8j6=iHere,Pja(MU)isthepredictiononitemafortheuserujusingthemodelMU,isathresholdthatcanbe00.20.40.60.811101201301401501601701801901100111011201130114011501Ranks of the users by NUPDNormalizedNUPD(a)00.040.080.120.160.2300301302303305#of ratings of the selected usersNormalized NUPD(b)Figure2:(a)Distributionofin\ruence.(b)NUPDvaluesofagroupof20userswhohaveratedalmostthesamenumberofitems.tiedwiththesmallestpredictionchangeperceivabletotheusersviatheavailableuserinterface.Asanexample,smallestpredictionchangeaMovieLensuserwouldnoticeis0.5orahalf-star.Inessence,theexpressionforNPDuisayshowmanytimesthepredictionswouldchangebeyondsomethresholdifwebuildthemodelwithouttheuserui.NPDuiisthein\ruencelevelofuserui.ThereisaproblemwithNPDui:ifthegroupofusers,whogetaectedbyui'sremoval,needpredictionsonmanyitems,uicouldexhibitpossessingalargeNPDui.Toovercomethisproblem,weproposeanotherversionofthisdenitionandcallitNUPDui.NUPDuicountsthenumberofuniqueuserswhosepredictions'gotchangedbyatleastthethresholdamountaswekeepthei-thuseroutduringmodel-building.AsisevidentfromthedenitionofNUPDui,itisequallyapplicabletoanyCFalgorithm,providedthatwehavethehistoricaldatatocomputeitfrom.NoticethatastraightforwardcomputationofNUPDscanbecomeveryexpensive;ifwearetocom-puteNUPDonlineorinaregularbasis,weneedtondacheaperway.Section6detailssuchanendeavor.4.1TheNatureofIn\ruence.Figure2(a)showsnormalizedNUPDvaluesofthetop1500in\ruentialusersandhighlightsthefactthatonlyahandfuloftheuserspossesshighin\ruence.ThisistrueforbothauthorityandNUPDmeasures.Theshapesdemonstratethepower-laworaZipf-likedistribution.Asimilarshapeisreportedin[5]forELPNetworkValues.NotethatthecorrelationbetweenauthorityandNUPDis0.96.5BuildingaPredictiveModelAsstatedbefore,NUPDsuersfromadrawback:thecomputationisquitetimeconsuming.Inordertocircumventthislimitation,weseekapredictivemodelthatcanprovideusers'in\ruencelevelsonthe\rywhile maintaininggoodaccuracy.AlthoughthecorrelationcoecientbetweenNUPDandthenumberofratingsis0.75,gure2(b)showsthattheamountofin\ruencecanvarywidelybetweenuserswhohaveratedapproximatelythesamenumberofmovies.Thissuggestswelookforamodelthatcanaccountforfactorsnotcapturedbythenumberofratings.Inthefollowingsectionwecompilealistofquali-tativefactorsthatseemtoaectin\ruencelevels.5.1QualitativeFactorsNumberofratings:Thisisthemostimmediatefactoronewouldpossiblycomealongwith.Ifauserratesmoreitems,shehasagreaterchancetobeclosetomanyusers.Moreover,suchausercanbeusefultomanyuserswhoarelookingforrecommendationsforawidevarietyofitems.Degreeofagreementwithothers:Thismeasureattemptstoestimateonaveragehowmuchauseragreestotheaverageopinionofothers:1=kPk=1jRia Raj.Thisexpressioncomputestheextenttowhichtheuserui'sratingsareswayedfromeachofthecorrespondingitem'saveragerating.Rarityoftherateditems:ThisisameasureverysimilartothatoftheInverseDocumentFre-quency(IDF),whichpenalizesfrequentitems,astheyareconsideredtohavelittlediscriminatingpower:1=kPj2Iui1=freq(j);where,Iuiisthesetofitemsthatuseruihasrated.Standarddeviationinone'srating:Thisamountstothedegreeauser'sratingsdeviatefromherrating-average.Theimplicationisthatahigherstandarddeviationcontributesagreatervaluethroughtheterm,(Rik Ri)inequation3.1.Degreeofsimilaritywithtopneighbors:Thisistheaveragesimilarityweightofthetopkneighborsofauserui:1=kPk=1Wij.Thisfactorcanbeassociatedwithtwoopposingimplications:usershavinghighervaluesfromthisexpressionmightbeabletoexertmoreeecttobein\ruential;whereas,ausermightbeeasiertoreplaceifsheisverysimilartoanumberofotherusers.Aggregatedpopularityoftherateditems:Ifthesumofthepopularitiesoftherateditemsishighenough,theuserhasagreaterchancetohaveoverlappeditemswithmanyusers.AggregatedMoviePopularity*Entropy:Entropyofamoviesimplyindicatesthedispersionoftheratingsitreceived.Multiplyingthiswiththepopularityofthemoviegivesameasurethattriestobalancebetweenpopularityandvariance.5.2TheRegressionModelWechosetouseSVMRegression(SVR)forourmod-eling.SVMsfollowtheStructuralRiskMinimizationPrinciplewhichseekstominimizeanupperboundonthegeneralizationerrorratherthantheprincipleusedinmostofthelearningmachines:EmpiricalRiskMinimizationPrinciple{minimizingthetrainingerror.Hence,SVMshavebeenshowingbettergeneralizationinmanyresults.Althoughmostofthepracticalus-agesforSVMsusedtobeinclassicationproblems,SVMshavebeenextendedtosolvenon-linearregres-sionproblems,mostlybecauseoftheintroductionofthe"-insensitivelossfunction[11];andtheresultingre-gressionmethodcalled" SVR.Wehavetriedvariouskernelfunctionstoperformthenon-linearmappingfromtheinputspacetothefeaturespace.However,theradialbasisfunction(RBF)producedthebestregressionresult.Inordertoselectthevaluesoftheparameters,Cand",across-validationapproachwascarriedout.Wehaverandomlyselected2416users(40%ofthetotal)andpartitionedthemintotrainingandtestsetsbya8:2split.libsvm[10]wasusedtogenerateregressionmodelsusingthefollowing:thesevenfactorsoutlinedbeforeaspredictors(independentvariables),anRBFkernel," SVR,andtheparameters,Cand".Themodelgaveasquaredcorrelationcoecientof0.94.Figure3showsthepredictionperformancebyplottingpredictedNUPDsagainstthecorrespondingactualNUPDstakenfromthetest-set.Ave-foldcrossvalidationwascarriedouttoensuretheresults'validity.Table1hastheregressionresultsaswellasafewstatisticsoftheactualNUPDvaluesinthetestset,averagedoverthevefolds.6In\ruenceinanItem-basedAlgorithmWenowturntohowthein\ruencepicturelookswhenusinganotherpredictionalgorithminordertoseehowalgorithm-dependentourmeasuresare.Theitem-itemAlgorithm.ThekNNbasedCFalgorithmproposedin[4]isdierentinmanywaysthantheuser-basedalgorithmwehaveaddressedsofar.Thealgorithmrstbuildsthemodelbycomputingitem-itemsimilarities.[4]proposedadjustedcosinemeasureforestimatingthesimilaritybetweentwoitemsi,andj:si;j=Pu2U(Ru;i Ru)(Ru;j Ru)qPu2U(Ru;i Ru)2Pu2U(Ru;j Ru)2Predictionforthe(user,item)pair,(u;i)iscomputedas:Pallsimilaritems;N(si;NRu;N)=P(jsi;Nj).Wecouldnotemployauthorityonthisalgorithm,asitisnotquitestraightforwardtoestablishdi- 010020030040050060070080090010001112131415161718191101111121131Test data pointsNUPD valuesFigure3:PerformanceofSVMregressionforNUPDonuser-useralgorithm.Thedottedlineshowstheactualvalues;whereas,thecontinuouslinerepresentsthepredictedvalues.rectedgesbetweenusers.WecouldnotcomputeELPNetworkValuesonthisalgorithmeither,sinceELPNetworkValuesinvolvethenotionofhowneigh-borsaectauser,andcorrespondingprobabilitycom-putationsbasedonthis.However,applyingNUPDbyHide-one-Usermethodwaseasy.WehaveestimatedNUPDsforthesamesetofuserswehaveselectedfortheuser-basedapproach.Modelingwith" SVRgaveaverygoodperformance:squaredcorrelationcoecientwas0.989.7ConclusionInthispaper,wehavecontinuedtheinvestigationintoin\ruenceinrecommendersbegunin[5].Wehaveshownthathowmanyopinionsauserexpressesisanimportantcomponentofin\ruence,butnotthewholestory.Wehavedenedseveralplausiblein\ruencemetricsandshownthatingeneral,theycorrelatestrongly.Webelieveourproposedmetric,NUPD,isexplain-ablebothtoresearchersandoperatorsofrecommendersystems.NUPDisalsoalgorithmindependent|itap-pliestoanyrecommendersystemalgorithmthatmakespredictions.NUPDiscomputationallyinecient.How-ever,wehavedemonstratedhowtobuilddataset-andalgorithm-specicregressionmodelsthatallowfortherapid,accurateestimationofauser'sin\ruence.Muchremainstobedone.Researchisneededtounderstandhowtheroleofin\ruencechangesit.Forinstance,whenin\ruenceisusedtohelpretailerssellproductsitmayhaveverydierentcharacteristicsthanwhenitisusedtoencouragecommunitymemberstocontributeopinions.Anotherrichareaofresearchisininterfacesforcommunicatingin\ruencetocommunitymembers.Theinterfaceislikelytoimpactboththein-terpretationofin\ruenceanditseectivenessinchang-ingbehavior.References[1]S.Wasserman,K.Faust,SocialNetworkAnaly-sis:MethodsandApplications,CambridgeUniversityPress,(1994).[2]P.Resnick,N.Iacovou,M.Sushak,P.Bergstrom,andJ.Riedl,Grouplens:Anopenarchitectureforcollaborativelteringofnetnews,inProceedingsofCSCW1994,ACMSIGComputerSupportedCoop-erativeWork,1994.[3]J.S.Breese,D.HeckermanandC.Kadie,Empiricalanalysisofpredictivealgorithmsforcollaborativelter-ing,inProceedingsoftheFourteenthAnnualConfer-enceonUncertaintyinAI,July1998.[4]B.M.Sarwar,G.Karypis,J.A.Konstan,andJ.Riedl,Item-basedcollaborativelteringrecommenda-tionalgorithms,inProceedingsofthe10thInterna-tionalWorldWideWebConference(WWW10),HongKong,May2001.[5]P.DomingosandM.Richardson,MiningtheNet-workValueofCustomers,ProceedingsoftheSeventhInternationalConferenceonKnowledgeDiscoveryandDataMining,SanFrancisco,CA,2001.ACMPress,pp.57{66.[6]L.Kleinberg.Authoritativesourcesinahyperlinkedenvironment,JournaloftheACM,46,1999.[7]L.Page,S.Brin,R.Motwani,andT.Winograd.ThePageRankcitationranking:Bringingordertotheweb,TechnicalReport,StanfordUniversity,Stanford,CA.1998.[8]D.Kempe,J.Kleinberg,andTardos,Maximizingthespreadofin\ruencethroughasocialnetwork,inProceedingsoftheninthACMSIGKDDinternationalconferenceonKnowledgediscoveryanddatamining,WashingtonDC,2003,pp.137{146.[9]K.Wang,andM.Y.T.Su,ItemSelectionby\Hub-Authority"ProtRanking,inSIGKDD'02,Canada.[10]C.C.Chang,andC.J.Lin,LIBSVM:alibraryforsupportvectormachines,2001.[11]V.N.Vapnik,TheNatureofStatisticalLearningTheory,NewYork,Springer-Verlag,1995.Table1:RegressionresultsonbothCFalgorithmsUser-UserItem-ItemRegressionerformanceMAE15.2630.6Sq.corr.coe.0.940.99MSE10362252.6NUPDTestSetAvg.81.57405.6Min00Max9802487StdDev123.25454.6
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