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http://dx.doi.org/10.7472/jksii.2019.20.4.73

Development of Journal Recommendation Method Considering Importance of Decision Factors Based on Researchers' Paper Publication History  

Son, Yeonbin (Department of Industrial and Management Engineering, Kyonggi University)
Chang, Tai-Woo (Department of Industrial and Management Engineering, Kyonggi University)
Choi, Yerim (Department of Industrial and Management Engineering, Kyonggi University)
Publication Information
Journal of Internet Computing and Services / v.20, no.4, 2019 , pp. 73-79 More about this Journal
Abstract
Selecting a proper journal to submit a research paper is a difficult task for researchers since there are many journals and various decision factors to consider during the decision process. For this reason, journal recommendation services are exist as a kind of intelligent research assistant which recommend potential journals. The existing services are executing a recommendation based on topic similarity and numerical filtering. However, it is impossible to calculate topic similarity when a researcher does not input paper data, and difficult to input clear numerical values for researchers. Therefore, the journal recommendation method which consider the importance of decision factors is proposed by constructing the preference matrix based on the paper publication history of a researcher. The proposed method was evaluated by using the actual publication history of researchers. The experiment results showed that the proposed method outperformed the compared methods.
Keywords
journal recommendation; importance by decision factors; paper publication history; machine learning; data analysis;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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