Abstract
Collaborative research is becoming increasingly important because it yields effective results and helps difficult research projects run smoothly. Previous studies have proposed many kinds of collaborator recommendation methods based on research features, such as specialty fields. However, few studies have constructed systems in which users can discover experts who have similar research interests using recommendation techniques. This paper proposes a novel researcher search system where users can efficiently discover potential candidates whose work locations are near theirs. Researchers are visualized on a map by our proposed system and users can use researcher’s names and research keywords to narrow down the search. Specifically, given a researcher’s name as a query, the system displays its relevant individuals based on either one of the following measures among researchers: research content similarity or collaborative relationship similarity. Our experiments demonstrated that recommendation results of these two similarity measures are minimally overlapped one another, indicating that our system could potentially help researchers discover collaborator candidates.
This research was partly supported by JST ACT-I (Grant number: JPMJPR18UC), JST ACT-X (Grant number: JPMJAX1909), and JSPS KAKENHI (Grant number: 20H04484).
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Takahashi, T., Tango, K., Chikazawa, Y., Katsurai, M. (2020). A Novel Researcher Search System Based on Research Content Similarity and Geographic Information. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_36
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