Abstract
Finding the best publication for article submission is a challenge because of the need to ensure that the publication is relevant. The relevancy of the publication is important because an article published in a less relevant publication will have less exposure to the intended target audience.
Publications can be characterized by a “finger print” or “signature” that can be a composition of the abstracts of articles already accepted by these publications. In turn, these abstracts can be characterized by vector representations obtained through the methods of natural language processing. Using publication abstract and the obtained finger print to search the publication space for the best fit is the aim of this research. There is a lack of substantial research related to discovering an recommending appropriate journals to those seeking to publish research papers. In this research, we extend the current state of the art and propose a robust and generic article matching tool for all publishers, using Web of Science data.
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Michail, S., Hazir, U., Alkan, T.Y., Ledet, J.W., Gunay, M. (2021). A Recommendation System for Article Submission for Researchers. In: Hemanth, J., Yigit, T., Patrut, B., Angelopoulou, A. (eds) Trends in Data Engineering Methods for Intelligent Systems. ICAIAME 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-79357-9_13
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DOI: https://doi.org/10.1007/978-3-030-79357-9_13
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