정보관리학회지 (Journal of the Korean Society for information Management)

  • 제33권1호
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  • Pages.317-336
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  • 2016
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  • 1013-0799(pISSN)
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  • 2586-2073(eISSN)
한국정보관리학회 (Korean Society for Information Management)
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TPIPF로 계산된 이용자프로파일을 적용한 논문추천시스템에 대한 연구

A Study on Scientific Article Recommendation System with User Profile Applying TPIPF

  • 장령령 (전남대학교 대학원 문헌정보학과) ;
  • 장우권 (전남대학교 문헌정보학과)
  • 투고 : 2016.03.05
  • 심사 : 2016.03.22
  • 발행 : 2016.03.30
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⟨ 이전 논문 다음 논문 ⟩

초록

오늘날 폭발적인 정보의 증가로 이용자들은 자신이 원하는 정보를 찾기 위해 엄청난 시간과 노력을 기울여야 한다. 이 문제를 해결하기 위하여 이용자의 정보요구를 분석하고 이용자에게 적합한 논문을 추천해주는 논문추천시스템이 등장하고 있다. 그러나 대부분의 논문추천시스템은 논문추천시스템의 핵심인 이용자 프로파일을 간과하고 있다. 따라서 이 연구는 논문추천시스템의 성능을 좌우하는 이용자 프로파일을 기존의 평균으로 계산하지 않고 새로운 TPIPF(Topic Proportion-Inverse Paper Frequency)로 계산하는 방법을 제안하였다. 제안된 방법과 기존의 방법을 모두 논문추천시스템에 적용하여 각각의 성능을 온라인 참고문헌 관리도구인 CiteULike에서 제공된 데이터 실험을 통하여 비교하였다. 그 결과 제안된 TPIPF 방법을 적용한 논문추천시스템의 성능이 더 높다는 것을 알 수 있었다.

Nowadays users spend more time and effort to find what they want because of information overload. To solve the problem, scientific article recommendation system analyse users' needs and recommend them proper articles. However, most of the scientific article recommendation systems neglected the core part, user profile. Therefore, in this paper, instead of mean which applied in user profile in previous studies, New TPIPF (Topic Proportion-Inverse Paper Frequency) was applied to scientific article recommendation system. Moreover, the accuracy of two scientific article recommendation systems with above different methods was compared with experiments of public dataset from online reference manager, CiteULike. As a result, the proposed scientific article recommendation system with TPIPF was proven to be better.

키워드

참고문헌

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