基于用户动态兴趣的微博个性化推荐的研究.docx

基于用户动态兴趣的微博个性化推荐的研究
基于用户动态兴趣的微博个性化推荐的研究
Abstract
WiththewidespreadusageofsocialmediaplatformssuchasWeibo,personalizedrecommendationshavebecomeincreasinglyimportantforenhancinguserexperiences.Inthispaper,weproposearesearchframeworkforpersonalizedWeiborecommendationsbasedonusers'dynamicinterests.Theframeworkincorporatesuserbehaviormodeling,interestdiscovery,andrecommendationgeneration.WeconductexperimentsonalargedatasetcollectedfromWeiboandevaluatetheproposedframeworkusingvariousmetrics,includingprecision,recall,andF-score.Theresultsdemonstratethatourapproachsignificantlyoutperformstraditionalcollaborativefilteringandcontent-basedmethodsintermsofrecommendationaccuracy.
1.Introduction
Weibo,asapopularsocialmediaplatform,attractsmillionsofactiveuserswhosharevarioustypesofinformationsuchastext,images,andvideos.Toproviderelevantandinterestingcontenttoeachuser,personalizedrecommendationshavebecomeacorefeatureofWeibo.However,traditionalrecommendationsystemsprimarilyfocusonusers'long-terminterests,whichmayfailtocaptureusers'dynamicpreferencesandinterestsinatimelymanner.Consequently,itisnecessarytodevelopapersonalizedrecommendationsystemthatcanadapttousers'changinginterests.
2.RelatedWork
Previousresearchonpersonalizedrecommendationsinsocialmediaplatformshasmainlyfocusedoncollaborativefilteringandcontent-basedmethods.Collaborativefilteringapproachesexploituser-iteminteractiondatatodiscoverusers'preferences.However,thesemethodssufferfromthesparsityproblemandfailtocapturetemporaldynamics.Content-basedmethods,ontheotherhand,analyzethecontentofitemstogeneraterecommendations.However,theyoftensufferfromthecold-startproblemfornewlyregisteredusers.Toaddresstheselimitations,researchershaveexploredapproachesthatleverageusers'socialconnectionsandincorporatetemporaldynamics.
3.ResearchFramework
OurproposedresearchframeworkforpersonalizedWeiborecommendationsbasedonusers'dynamicinterestsconsistsofthreemaincomponents:userbehaviormodeling,interestdiscovery,andrecommendationgeneration.
3.1Use