IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v128y2023i1d10.1007_s11192-022-04535-y.html
   My bibliography  Save this article

Deep learning for journal recommendation system of research papers

Author

Listed:
  • Esra Gündoğan

    (Fırat University)

  • Mehmet Kaya

    (Fırat University)

  • Ali Daud

    (Abu Dhabi School of Management)

Abstract

Many journals belonging to different publishers have emerged with the advancements in research. The increase in the number of scholarly journals has made it difficult for researchers to choose the correct journal for publishing their articles. Submitting an article to the correct journal is very important in terms of academic sharing and for shortening the publication time of the article. It is time consuming to determine the most suitable journal in scope among thousands of journal choices for the user. Therefore, journal recommendation systems have been an important tool for researchers. Recommendation systems generally depend on the user's publications, relationships with other authors, etc. The fact that it is based on features makes it not useful for users who are new to the research field. In this study, an approach that recommends a journal is proposed by using the title, abstract, keyword and reference information of the article, without the need of users’ information. Unlike other studies, the scope information of the journals is needed to determine the appropriate journals for the article, which is usually obtained from the articles previously published in the related journals. The publications of the journals in the last 3 years have been used to determine the scope of the journal. Unlike the publishers' journal recommendation systems developed so far, this study is a comprehensive recommendation system that includes journals from more than one publisher. In this approach, SBERT has been used to find the similarity of the scope of journals with articles. When the results are compared with the Word2vec, Glove and FastText, which are often the preferred methods in document similarity, it was observed that sentence-level similarity-based recommendations with SBERT are more successful. The experimental results show the effectiveness of our approach.

Suggested Citation

  • Esra Gündoğan & Mehmet Kaya & Ali Daud, 2023. "Deep learning for journal recommendation system of research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 461-481, January.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:1:d:10.1007_s11192-022-04535-y
    DOI: 10.1007/s11192-022-04535-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04535-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-022-04535-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thushari Silva & Jian Ma & Chen Yang & Haidan Liang, 2015. "A profile-boosted research analytics framework to recommend journals for manuscripts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(1), pages 180-200, January.
    2. Gianfranco Lombardo & Michele Tomaiuolo & Monica Mordonini & Gaia Codeluppi & Agostino Poggi, 2022. "Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics," Future Internet, MDPI, vol. 14(1), pages 1-21, January.
    3. Zafar Ali & Guilin Qi & Pavlos Kefalas & Shah Khusro & Inayat Khan & Khan Muhammad, 2022. "SPR-SMN: scientific paper recommendation employing SPECTER with memory network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6763-6785, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chi Jiang & Xiao Ma & Jiangfeng Zeng & Yin Zhang & Tingting Yang & Qiumiao Deng, 2023. "TAPRec: time-aware paper recommendation via the modeling of researchers’ dynamic preferences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3453-3471, June.
    2. Yi Zhang & Chengzhi Zhang & Philipp Mayr & Arho Suominen, 2022. "An editorial of “AI + informetrics”: multi-disciplinary interactions in the era of big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6503-6507, November.
    3. Mark Bukowski & Sandra Geisler & Thomas Schmitz-Rode & Robert Farkas, 2020. "Feasibility of activity-based expert profiling using text mining of scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 579-620, May.
    4. Huang ZhengWei & Min JinTao & Yang YanNi & Huang Jin & Tian Ye, 2022. "Recommendation method for academic journal submission based on doc2vec and XGBoost," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2381-2394, May.
    5. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Alhoori, Hamed & Furuta, Richard, 2017. "Recommendation of scholarly venues based on dynamic user interests," Journal of Informetrics, Elsevier, vol. 11(2), pages 553-563.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:128:y:2023:i:1:d:10.1007_s11192-022-04535-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.