Latent topical authority indexing = 잠재적 주제별 영향력 색인

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dc.contributor.advisorOh, Alice-
dc.contributor.advisor오혜연-
dc.contributor.authorKim, Jooyeon-
dc.contributor.author김주연-
dc.date.accessioned2017-03-29T02:40:20Z-
dc.date.available2017-03-29T02:40:20Z-
dc.date.issued2016-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=649699&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/221886-
dc.description학위논문(석사) - 한국과학기술원 : 전산학부, 2016.2 ,[iv, 25 p. :]-
dc.description.abstractMuch of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority, such as the h-index, to find the prominent authors, and use these authors as starting points and waypoints. However, these authority indices do not differentiate authority based on research topics, so additional effort is needed to find the prominent authors in the relevant research topics. This thesis presents Latent Topical-Authority Indexing (LTAI), a Bayesian topic model that discovers the topical authority of scholars by jointly modeling the topics, authority, and citation network. In this thesis, four academic corpora are fitted to LTAI: CORA, Arxiv Physics, PNAS, and Citeseer. This thesis shows that explicitly modeling topical authority leads to improved accuracy over other recent models when predicting citations and authors of publications.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjecttopic-
dc.subjectmodeling-
dc.subjectauthority-
dc.subjectcitation-
dc.subjectnetwork-
dc.subject토픽-
dc.subject모델링-
dc.subject영향력-
dc.subject인용-
dc.subject네트워크-
dc.titleLatent topical authority indexing = 잠재적 주제별 영향력 색인-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :전산학부,-
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CS-Theses_Master(석사논문)
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