Recommender System in the Process of Scientific Peer Review in Mathematical Journal

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Published: 28.05.2020
UDC 004.021 004.42
DOI: https://doi.org/10.26907/1562-5419-2020-23-4-708-732
Issue
Vol. 23 No. 4 (2020): Special issue «Scientific Services & Internet». Part 2

Main Article Content

Alexander Mikhailovich Elizarov
N. I. Lobachevskii Institute of Mathematics and Mechanics, Kazan (Volga Region) Federal University, ul. Kremlyovskaya, 35, Kazan, 420008; Higher School of Information Technologies and Intelligent Systems, Kazan (Volga Region) Federal University, ul. Kremlyovskaya, 35, Kazan, 420008
https://orcid.org/0000-0003-2546-6897
Evgeny Konstantinovich Lipachev
N. I. Lobachevskii Institute of Mathematics and Mechanics, Kazan (Volga Region) Federal University, ul. Kremlyovskaya, 35, Kazan, 420008; Higher School of Information Technologies and Intelligent Systems, Kazan (Volga Region) Federal University, ul. Kremlyovskaya, 35, Kazan, 420008
https://orcid.org/0000-0001-7789-2332
Shamil Makhmutovich Khaydarov
N. I. Lobachevskii Institute of Mathematics and Mechanics, Kazan (Volga Region) Federal University, ul. Kremlyovskaya, 35, Kazan, 420008
https://orcid.org/0000-0002-6835-3289

Abstract

An approach is proposed for organizing expert evaluation of a scientific document submitted to a mathematical journal. Domain restriction is associated with the use of the Mathematical Sciences Classification System – MSC. A recommendation system is presented that allows you to create a list of possible experts for conducting scientific peer-reviewing on a mathematical article. The recommender system uses the MSC codes presented by the author of the article on the MSC2020 classifiers. If the codes MSC2000 or MSC2010 are indicated in the article, they are automatically converted to codes MSC2020. For each expert, the system supports a personal profile that contains a set of codes MSC2020, supplemented by numerical characteristics – weights calculated for each code in accordance with the system of accounting for competencies, preferences or refusals to participate in the review procedure. This set is automatically edited if the expert is included in the list of possible reviewers – the weights of several codes increase or decrease, as well as new codes are added. The recommendation system is implemented as an integrated tool (plug-in) of the Open Journal Systems (OJS) platform. The developed method has been tested in the information system of the Lobachevskii Journal of Mathematics (https://ljm.kpfu.ru).

Article Details

Author Biographies

Alexander Mikhailovich Elizarov

Doctor of Physics and Mathematics, Professor, Honoured Worker of Science of the Republic of Tatarstan, Kazan Federal University. Current scientific interests: data mining, recommender systems, cloud computing, knowledge extraction technologies.

Evgeny Konstantinovich Lipachev

Candidate of Physics and Mathematics, Associate Professor, Kazan Federal University. Current scientific interests: data mining, recommender systems, cloud computing, knowledge extraction technologies.

Shamil Makhmutovich Khaydarov

Assistant, Department of Computer Mathematics and Informatics, Institute of Mathematics and Mechanics N.I. Lobachevsky Kazan (Volga) Federal University. Current scientific interests: data mining, recommender systems, cloud computing, knowledge extraction technologies.

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