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merge
Animprovedexplicittrust-basedmethod
ABSTRACT
Collaborativefiltering(CF)iswidelyusedtechniquetogenerate
recommendations,butitsuffersfromtwoissues:
datasparsity(itreferstotheproblemthattheuserscanrateonlyalimitednumbersofitems)andcoldstart(itreferstothedifficultytobootstrappingtheRecommender
Systemsforthenewusersandnewitems).Trust-awareRecommender
Systemsmakecontributiontoaddressingthetwoissueseffectively,butit’salsoabigchallengetomergethetrustinformationintoCFtoachievebetterperformance.Inthispaper,weprovideanimprovedtrustvaluecomputationmethodtoincorporatetheimplicittrustwithexplicittrust.Ourmainideaistoutilizethetargetuser’sreviewsratingtoinferitsimplicittrustinthesystem,whichenhancetheexplicittrust-basedtechniquesbymergingusers’interactionexperiencewiththetargetuser’scontributiontoalloftheuserswhorateitsreviews.Wededicatetoresolvethepreviouslymentionedproblems.Experimentalresultsshowourmethodiseffectiveintermsofaccuracywhilepreservingagoodcoverage.
1Introduction
Inrecentyears,trusthasbeenstudiedinnumbersofresearchand
ithasbeenprovedtobeeffectiveinreducinginformationoverload.Severaltrust-basedmethodshavebeenproposedtoaddressthedatasparsityandrecommendationaccuracyproblem[9,].Trust-awarerecommendersystemsallowausertorateotherusers,thesystemcanquicklymakerecommendationsusingexplicitneighborset,andatrustmetric[8]reliesonaweboftrustfordefiningavalueforhowmuchausercantrustotherusersinthesystem.Thereareseveralapproachesarepresentedusingexplicittruststatementsin[10,3,11].TidalTrustisanalgorithmforinferringtrustrelationships,whichisusedin[10].Theactiveuserasksitstrustedneighborsforatrustratingforthetargetuser,andthencalculatesaweightedaverageoftrustratingfromtheneighborstothetargetuser.TidalTrust[1]isusedasthebasisforgeneratingpredictiveratingspersonalizedforeachuser.Theaccuracyoftherecommendedratingsisshowntooutperformbothasimpleaverageratingandtheratingsproducedbyacommonrecommendersystemalgorithm.RayandMahantiarguethattrustedneighborsmayhavedifferentpreferencesandbyremovingthetrustlinkswithlowsimilaritycanfurtherimprovetheperformance[2].Anothermethodistomergetheratingsoftrustedneighborstorepresentthepreferenceoftheactiveuser[3],andaccordingtothemergedratingsetfindsimilarusers,thenincorporatethesimilarusersandtrustedneighbors’ratingstopredicttheratingoftheactiveusers.Experimentalresultsonreal-worlddatasetsshowthatexistingtrustmetricscannotprovidesatisfyingperformance[6],andindicatethatfuturemetricsshouldbedesignedmorecarefully.
Ontheotherhand,implicittrustisbasedonthepastratingbehaviorofindividualprofilesratherthantheusers’directexperience.
TherearemanyotherapproachesusingImplicittrustinferredfromuserpastbehaviors,JO’Donovan&BSmythproposeanumbersofcomputationalmodelsoperatingattheprofile-level(averagetrustfortheprofileoverall)andattheprofile-item-level[4](averagetrustforaparticularprofilewhenitcomestomakingrecommendationsforaspecificitem).Theydescribehowtrustinformationcanbeincorporatedintotherecommendationprocessanddemonstratethatithasapositiveimpactonrecommendationquality.NLathia,SHailes&LCaprapresentatrustedk-nearestrecommendersalgorithm,itallowstheuserexploittheratinginformationoftheotherusers,fromwhichtheycanlearnwhoandhowmuchtotrustoneanother.GuibingGuo1,JieZhang1,DanielThalmann1,AnirbanBasu2,NeilYorke-Smithconductanempiricalstudytoexploretheabilityoftrustmetrics[6]todistinguishexplicittrustfromimplicittrustandtogenerateaccuratepredictions.
Explicittrustreflectsthedegreetowhichtheusers’satisfactionswithotherstheyhavedirectexperience.Butinlarge-scaleenvironments,directexperienceisoftennotsufficientorevennon-existent.Althoughspecifictrustvaluesarepossibleinrealsystems,theamountoftrustinformationisrelativelylittlecomparedtothenumberofratings.Ontheotherhand,Insuchcases,predictionisbasedonuser’s“indirectexperience”–opinionsobtainedfromotheragents.Thesemethodsusingexplicittrustbasedonthetransitiveintermofthetopicoftrustwhichisinherentlydoubtful,andwhetheritiseffectiveornot,trustpropagationdoesnotprovidesignificantbenefits.Previouslymentionedapproachescannotimprovetheaccuracyaswellascoverage.
Inthispaper,ourmainideaistofindaneffectivemethodtoimprovetheperformanceoftheexplicittrust-basedapproachbymergingtheimplicittrustinformationaccordingtothereviewsratingofthetargetuser.Thisistosay,wecomputetheimplicittrustvalueofthetargetuserifitisinthetrustedneighborsetoftheactiveuserotherthansimplycalculatefromthetrustedfriends.Moreover,wesearchfortheoptimalportiondistributingtheexplicitandimplicittrust.
2TheMergeMethod
Animportantclassificationoftrustmetricsisinglobalandlocalones.Localtrustmetricstakeintoaccounttheverypersonalandsubjectiveviewsoftheusersandpredictdifferentvaluesoftrustmetricsinotherusersforeverysingleuser.Insteadglobaltrustmetricspredictaglobal“reputation”valuethatapproximateshowmuchthecommunityasawholeconsidersacertainuser[7].Andinourpaper,wetaketheexplicittrustasthelocaltrustconsideringpersonalbias.Animportantfactwemusttakeintoaccountistheratingranges,duetothediversityofusers’habits,moodandcontexts,therangeofratingsgivenbyauserisprobablydifferentforanother.InadditionthattheExtendedEpinionsdatasetweuseonlycontaintwovalues-1fordistrustand1fortrust,whichcannotfullyexpresstherangeoftrustworthyoftheusers’towardsanother.Herewetaketheimplicittrustastheglobaltrusttocopewiththecold-startproblemfortrustedneighbors,weproposeanapproachtoinferimplicittrustfromusers’ratingprofiles,asimplicitissymmetric,whileoneaspectoftrustisasymmetric,mergingexplicitandimplicittrustcanovercometheweaknessaswellascoldstaruser.
2.1Implicittrustmeasurement
Inthispaper,weprovideanimprovedmethodmergingtheexplicitandimplicittrusttoimprovetheoverallperformanceofrecommendationandmitigatethecold-startproblem.Firstlyinthetrustnetworkwecangettheactiveuser’sdirecttrustneighborssetandthetrustvalues.Thenwecomputetheimplicittrustvaluescorrespondingtothesetandmergetheexplicitandimplicittrusttogetthetrustvalues,finallyweaveragetheratingsaccordingtothetrustvaluesofthetrustedneighbors.
ThedatasetweexploitinourexperimentsisderivedfromtheEWebsite,ExtendedEpinionsdateset.UserscanexpresstheirWebofTrust,reviewswhosereviewsandratingstheyhaveconsistentlyfoundtobevaluable.Inferringfromtheusers’pastbehavior,itisverylikelythatthemorehelpfultheusers’reviewsare,themoretrusttheymayreceiveastheymaybegoodatusingthesystemandmakingcorrectrecommendations,andwetendtobelievetheyaremoretrustworthybecauseofthecontributionfortheotherstheymake.
Sowedefineatrustmetricbasedonthereviewsratingwhichisreflectionofthehelpfulnesstheactiveuserathinkthetargetuserbas.Therangeofratingscoresis1to5(1-Nothelpful,2-SomewhatHelpful,3-Helpful4-VeryHelpful5-MostHelpful),whilewecomputethehelpfulnessofthetargetuserforallitsreviewsinthesystem,andweregardthehelpfulnessasthetrustworthinessreceivedfromtheuserwhohaveratingforthereviews.itisalsotheimplicittrustorglobaltrustaswedefined.Findingthetrustneighborsetcorrespondingtowhichwesearchthereviewratingoftheeachneighbor.TakingneighboruserT1forexample,itiseasyforustolookupthereviewsratingscoresRi(i=1,2,3,4,5forthereviewratingscale)andallofitsreviewnumberninthesystem.NiisthenumberofratingRi.Wedefineasimplemethodfortheimplicittrustasfollows:
(1)
Theamountoftrustthatuserbpossessisequivalenttotheproportionoftimesthatbgenerateshelpfulrecommendationsovertheperformanceinthesystem.Thatistosay,theideahereisnottocomputeuserb’scontributiontoacertainuser,buttoalltheusersthatithavemaderecommendations.Wecallthistrustvalueasb’sglobaltrust.
2.2mergetheimplicitandexplicittrust
Giventheusersetin
whichrepresentsdirecttrustedneighborsoftheactiveusera,wethencomputetheeveryusers’implicittrustvalueas[1]correspondingtotheset.Morespecifically,weadoptasimplyandeffectivemergingmethodasfollows:
(2)
Where
istheoveralltrustvalue,andDTistheexplicittrustspecifiedbytheactiveusera,IDTistheimplicittrustvalueofbintermsofthesystem.
isaparameterwhichweneedtoexploretheeffectoftheparameter,wetunethevalue
from0to0.9withstep0.1.Finally,thepredictedratingpofitemifortheactiveuseraisgeneratedbyaveragingtheratingsaccordingtothetrustvaluesofthetrustedneighborsspecifiedbytheactiveusera,asfollows:
p=
(3)
Theneighborswhoaretrustedmorebyuseraandhaveahigherglobaltrustvaluewillhavehigherimpactonthepredictedratings.
2.3Analysisofthemergemethod
Onecommoncharacteristicofthedatasparsityandcold-startproblemsisthatthesmallnumberoftrustinformationspecifiedbytheactiveusermakeitdifficu