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改进协同过滤算法在服装个性化推荐的研究.docx

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改进协同过滤算法在服装个性化推荐的研究 Title:ImprovingCollaborativeFilteringAlgorithmsforPersonalizedClothingRecommendations Abstract: Withtheincreasingpopularityofonlineshopping,personalizedrecommendationshavebecomeanessentialcomponentine-commerce.Clothing,beingahighlypersonalizedandsubjectivechoice,presentsuniquechallengesforrecommendationsystems.Thisresearchaimstoimprovecollaborativefilteringalgorithmsinthecontextofpersonalizedclothingrecommendations.Byleveragingtheinherentcharacteristicsofclothingitemsanduserpreferences,theproposedenhancementsaimtodelivermoreaccurateandsatisfyingrecommendationstousers. Introduction: Inrecentyears,clothingrecommendationsystemshavegainedimmensepopularityduetotheconveniencetheyoffertoonlineshoppers.However,theeffectivenessandpersonalizationofthesesystemslargelydependonthequalityoftheunderlyingrecommendationalgorithms.Collaborativefiltering,awidelyusedrecommendationtechnique,oftenfallsshortwhendealingwiththeuniquecharacteristicsofclothingitems.Thisresearchintendstoaddresstheselimitationsandimprovetheperformanceofcollaborativefilteringforpersonalizedclothingrecommendations. 1.ChallengesinClothingRecommendation: Numerouschallengesarisewhenapplyingcollaborativefilteringalgorithmstoclothingrecommendations.Firstly,clothingitemsexhibitrichattributessuchascolor,material,style,andfit,makingitchallengingtocapturethevaryingpreferencesofindividualusers.Additionally,fashiontrendschangerapidly,andusers'preferencescanbeinfluencedbymultiplefactors,includingseason,occasion,andpersonalstyle.Lastly,userfeedbackinclothingrecommendationsisoftenscarce,leadingtolimiteddataavailabilityforaccuratepersonalizedrecommendations. 2.EnhancedUserProfileCreation: Improvingtheformationofuserprofilesiscrucialforbetterclothingrecommendations.Traditionalcollaborativefilteringapproachesviewuserpreferencesasbinary(liked/disliked),butclothingrecommendationsrequirefine-grainedpreferences.Byincorporatingadditionaluserattributeinformation,suchasbodytype,age,andfashionstyle,amorecomprehensiveu