一种基于相似性度量的综合推荐模型的开题报告.docx

一种基于相似性度量的综合推荐模型的开题报告

Title:AComprehensiveRecommendationModelBasedonSimilarityMeasurement

Introduction:

Withtheexplosivegrowthofinformationontheinternet,peoplearefacingthechallengeoffindingrelevantandpersonalizedcontent.Toaddressthisissue,recommendationsystemshavebeenwidelyappliedinvariousdomains,suchase-commerce,socialmedia,andentertainment.Amongtherecommendationmodels,similarity-basedmethodshavebeenproventobeeffectiveincapturinguserspreferencesandinterests.However,mostexistingsimilarity-basedmodelsfocusonasingletypeofdata(e.g.,itemattributes,userprofile,orinteractionhistory),whichmaynotfullyreflectthecomplexityofusersinterests.Therefore,developingacomprehensiverecommendationmodelthatintegratesmultipletypesofdataandperformssimilaritymeasurementacrossthemisdesirable.

Objectives:

Thegoalofthisprojectistodevelopacomprehensiverecommendationmodelthatcanleveragemultiplesourcesofdatatoprovidemoreaccurateanddiversifiedrecommendations.Specifically,theobjectivesare:

1.Toinvestigatethestate-of-the-artrecommendationmodelsthatutilizesimilarity-basedtechniquesandtheirlimitations.

2.Toproposeanovelcomprehensiverecommendationmodelthatintegratesmultipletypesofdataandperformssimilaritymeasurementacrossthem.

3.Toevaluatetheproposedmodelonreal-worlddatasetsandcompareitwithotherstate-of-the-artmethodsintermsofaccuracy,diversity,andnovelty.

Methodologies:

Theproposedmodelwillconsistofthreemaincomponents:datapreprocessing,similaritymeasurement,andrecommendationgeneration.

1.Datapreprocessing:Theobjectiveofdatapreprocessingistoobtaintherelevantinformationfromvarioussources,includinguserprofile,itemattributes,andinteractionhistory.Thecollecteddatawillbetransformedintoaunifiedformatandnormalizedtoeliminatetheeffectofscale.

2.Similaritym