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基于用户主题模型的微博用户兴趣挖掘(英文).docx

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基于用户主题模型的微博用户兴趣挖掘(英文) TopicModeling-BasedInterestMiningforWeiboUsers Introduction Withthegrowingpopularityofsocialmediaplatforms,suchasWeibo,peopleareprovidedwithaconvenientandefficientwaytoshareandacquireinformation.AsafamousmicrobloggingplatforminChina,Weiboisanidealplaceforuserstoexpressthemselves,makefriends,andkeepupwithcurrentevents.However,withthemassiveamountofinformationproducedeverydayonWeibo,itbecomesharderforuserstolocateandfilteroutthemostrelevantcontentaccordingtotheirinterests.Therefore,itisofgreatsignificancetodevelopaneffectiveinterestminingmethodforWeibouserstohelpthemdiscoverandfollowthecontentstheycareabout. Inthispaper,weproposeatopicmodeling-basedapproachtoextractuserinterestsfromWeibodata.Specifically,weaimtoconstructauser-interestprofilebyanalyzingtheirpostinghistoryandincorporatingtopicmodelingtechniquestoidentifythelatenttopicsoftheirtexts.Theunderlyingideaisthatuserstendtopostmessagesaboutthetopicsthattheyareinterestedin,andthetopicsextractedfromtheirmicroblogscanprovideinsightsintotheirpreferencesandtastes.Therefore,bydiscoveringthelatenttopicsoftheirtexts,wecanidentifytheuser'sinterestsandconstructapersonalizedrecommendationsystemonWeibo. Background Inrecentyears,interestmininginsocialmediahasattractedincreasingattentionfromresearchersduetoitspotentialapplicationsinvariousfields,suchaspersonalizedrecommendation,userprofiling,andcontentfiltering.Traditionalapproachestointerestminingoftenrelyonkeywordextractionorstatisticalanalysisofuserbehaviorsandinteractionpatterns.Forexample,somestudieshaveproposedtoextractkeywords,hashtags,andnamedentitiesfromtheuser'smicroblogsandusethemasindicatorsoftheirinterests(Yangetal.,2016).Otherstudieshaveusedmetricssuchasthefrequencyoflikes,comments,andrepoststomeasuretheuser'sengagementandinterestlevel(Zhangetal.,2014). However,thesemethodsmaysufferfromseverallimitations.Firstly,keyword-basedapproachesmaymissoutontheimplicitorobscureaspectsofauser'sinterests,sincetheyonlyfocusontheexplicitinformationinthete