Evaluation Index to Find Relevant Papers: Improvement of Focused Citation Count

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Abstract

During a research survey, it is very important to quickly find suitable papers. It is common practice for researchers to select relevant papers by searching using query keywords, ranking those papers by citation number, and checking in order from the highest ranked papers. However, if a paper that had a query keyword as a non-primary word had many citations, it would hinder any attempt to quickly find the appropriate paper. We have already proposed a Focused Citation Count (FCC) that supports the finding of suitable papers by setting the number of citations as a more appropriate evaluation index by properly focusing on cited papers which are the sources of citation counts. In this study, we propose an improved method of FCC. Since FCC is easily affected by the size of the cited number, this proposal aims to reduce its characteristics. We evaluate the proposed method using actual paper data and try to confirm its effectiveness.

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Nakatoh, T., & Hirokawa, S. (2019). Evaluation Index to Find Relevant Papers: Improvement of Focused Citation Count. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11569 LNCS, pp. 555–566). Springer Verlag. https://doi.org/10.1007/978-3-030-22660-2_41

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