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基于加权二部图的SlopeOne推荐算法.docx

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基于加权二部图的SlopeOne推荐算法 Title:SlopeOneRecommenderAlgorithmbasedonWeightedBipartiteGraph Abstract: Withthecontinuousgrowthofonlineinformation,theneedforpersonalizedandaccuraterecommendationshasbecomecrucial.SlopeOnerecommendationalgorithmhasproventobeeffectiveingeneratingpersonalizedrecommendationsbyleveraginguserratings.Inthispaper,weproposeanenhancedversionoftheSlopeOnealgorithmbasedonaweightedbipartitegraphapproach.Thealgorithmutilizesaweightedbipartitegraphtomodeltherelationshipsbetweenusersanditems,whichimprovesrecommendationqualitybytakingintoaccountbothusersimilaritiesanditemsimilarities.ExperimentalresultsdemonstratethattheproposedalgorithmachievesbetterrecommendationperformancecomparedtothetraditionalSlopeOnealgorithm. 1.Introduction: Therapiddevelopmentofe-commerceandcontentstreamingplatformshasmadeitchallengingforuserstodiscoverrelevantandpersonalizedinformation.Recommendersystemsaimtoaddressthischallengebyprovidinguserswithtailoredrecommendationsbasedontheirpreferencesandbehaviors.Collaborativefiltering(CF)isoneofthemostwidelyusedtechniquesinrecommendersystems.AmongCFalgorithms,SlopeOnehasgainedpopularityduetoitssimplicityandeffectiveness.However,theoriginalSlopeOnealgorithmdoesnotconsidertherelationshipsbetweenusersanditems,whichmaylimititsrecommendationaccuracy.Therefore,thereisaneedtoenhancetheSlopeOnealgorithmtoprovidemoreaccuraterecommendations. 2.RelatedWork: SeveralstudieshaveproposedmodificationstotheSlopeOnealgorithmtoimproveitsperformance.Forexample,WeightedSlopeOneincorporatesconfidenceweightstohandlesparseandunreliabledata.ImprovingtheaccuracyandscalabilityofSlopeOnehasalsobeenexploredbyintroducingmatrixfactorizationtechniquesandneighborhood-basedapproaches.However,thesemethodstendtobecomputationallyexpensiveandmaynotscalewellwithlargedatasets. 3.ProposedMethod: Inthispaper,weproposeanovelapproachtoenhancetheSlopeOnealgorithmusingaweightedbipartitegraph.Thealgorithmleveragesbothusersimilaritiesanditemsimilaritiestoimproverecommendationquality.The