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基于标签信息的个性化音乐推荐算法研究 Title:ResearchonPersonalizedMusicRecommendationAlgorithmBasedonLabelInformation Abstract: Withtherapiddevelopmentofdigitalmusicplatforms,thetaskofrecommendingpersonalizedmusictousershasbecomemorecritical.Traditionalrecommendationalgorithmsmainlyfocusonuserpreferencesandhistoricaldata,whileneglectingvaluablemetadatasuchaslabels.Thispaperaimstoinvestigateandproposeapersonalizedmusicrecommendationalgorithmthatleverageslabelinformationtoenhancetheaccuracyanddiversityofrecommendations.Weanalyzethesignificanceandchallengesofincorporatinglabelinformationintotherecommendationprocessandproposeanovelalgorithmthatcombinesuserpreferences,historicaldata,andlabelinformation.Experimentalresultsdemonstratethatouralgorithmoutperformstraditionalmethodsintermsofrecommendationaccuracyanddiversity. 1.Introduction 1.1Background 1.2ResearchObjective 2.RelatedWork 2.1TraditionalMusicRecommendationAlgorithms 2.2IncorporatingLabelInformationinMusicRecommendation 2.3ChallengesandLimitations 3.ProposedAlgorithm 3.1DataCollectionandPreprocessing 3.2CollaborativeFilteringwithLabelInformation 3.3UserPreferencesandHistoricalDataAnalysis 3.4FusionofLabelInformation 3.5AlgorithmDescription 4.ExperimentalEvaluation 4.1DatasetDescription 4.2EvaluationMetrics 4.3BaselineMethods 4.4ExperimentalResultsandAnalysis 5.Discussion 5.1ImplicationsofLabelInformationinMusicRecommendation 5.2LimitationsandFutureDirections 6.Conclusion 1.Introduction: 1.1Background Withtheriseofdigitalmusicplatforms,usersarenowoverwhelmedwithanextensivelibraryofmusic.Tohelpusersdiscovernewmusicthatalignswiththeirtastesandpreferences,musicrecommendationalgorithmshavebecomeanessentialcomponentoftheseplatforms.Suchalgorithmsaimtopersonalizethelisteningexperienceforindividualusersbysuggestingrelevantsongs,artists,orplaylistsbasedontheirhistoricaldataandpreferences. 1.2ResearchObjective Despitethesuccessoftraditionalrecommendationalgorithms,theymainlyrelyonuserpreferencesandhistoricaldata,therebyneglectingothervaluab