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A Study on Scientific Article Recommendation System with User Profile Applying TPIPF
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Abstract
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.
- keywords
- scientific article recommendation system, article profile, user profile, TPIPF, content-based filtering, LDA, 논문추천시스템, 논문 프로파일, 이용자 프로파일, TPIPF, 콘텐츠기반 필터링, LDA
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