IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i5d10.1007_s11192-022-04354-1.html
   My bibliography  Save this article

Recommendation method for academic journal submission based on doc2vec and XGBoost

Author

Listed:
  • Huang ZhengWei

    (China Three Gorges University)

  • Min JinTao

    (China Three Gorges University)

  • Yang YanNi

    (China Three Gorges University)

  • Huang Jin

    (China Three Gorges University)

  • Tian Ye

    (China Three Gorges University)

Abstract

With the continuous deepening of academic research in various disciplines and the continuous increase in the number of scientific researchers, exploring the mechanism of matching scientific research results and academic journal subjects is a key topic that can assist researchers in selecting suitable journals for submission. The classification and recommendation of academic journals based on a traditional text representation model cannot take advantage of the semantic relationship between words and cannot take into account the diversity of topics received by different journals, which affects the classification and recommendation effect. To solve these problems, this paper uses doc2vec to perform distributed representation of the bibliographic text so that the semantics between the text features are fully preserved. Then, the XGBoost algorithm is used to consider the impact of the different characteristics of the title, abstract, and keywords of the bibliography on the published journal. The academic journal submission recommendation model proposed in this paper can solve the problem that traditional methods cannot make full use of the contextual semantic information and improve the efficiency of scientific research personnel's academic achievement publications. Experiments on Common SCI English journals in the computer field show that when recommending three candidate journals, the accuracy rate reached 84.24%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:5:d:10.1007_s11192-022-04354-1
    DOI: 10.1007/s11192-022-04354-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04354-1
    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/s11192-022-04354-1?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. Xiangjie Kong & Huizhen Jiang & Zhuo Yang & Zhenzhen Xu & Feng Xia & Amr Tolba, 2016. "Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    3. Xi Chen & Huan-jing Zhao & Shu Zhao & Jie Chen & Yan-ping Zhang, 2019. "Citation recommendation based on citation tendency," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 937-956, 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. Kaiwen Shi & Kan Liu & Xinyan He, 2024. "Heterogeneous hypergraph learning for literature retrieval based on citation intents," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4167-4188, July.
    2. Chanathip Pornprasit & Xin Liu & Pattararat Kiattipadungkul & Natthawut Kertkeidkachorn & Kyoung-Sook Kim & Thanapon Noraset & Saeed-Ul Hassan & Suppawong Tuarob, 2022. "Enhancing citation recommendation using citation network embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 233-264, January.
    3. Qi Zhang & Rui Mao & Rui Li, 2019. "Spatial–temporal restricted supervised learning for collaboration recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1497-1517, June.
    4. Chaocheng He & Jiang Wu & Qingpeng Zhang, 2022. "Proximity‐aware research leadership recommendation in research collaboration via deep neural networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 70-89, January.
    5. Yongjun Zhu & Lihong Quan & Pei‐Ying Chen & Meen Chul Kim & Chao Che, 2023. "Predicting coauthorship using bibliographic network embedding," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 388-401, April.
    6. Xiangjie Kong & Huizhen Jiang & Wei Wang & Teshome Megersa Bekele & Zhenzhen Xu & Meng Wang, 2017. "Exploring dynamic research interest and academic influence for scientific collaborator recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 369-385, October.
    7. Jialiang Lin & Yao Yu & Jiaxin Song & Xiaodong Shi, 2022. "Detecting and analyzing missing citations to published scientific entities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2395-2412, May.
    8. 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.
    9. Kong, Xiangjie & Mao, Mengyi & Jiang, Huizhen & Yu, Shuo & Wan, Liangtian, 2019. "How does collaboration affect researchers’ positions in co-authorship networks?," Journal of Informetrics, Elsevier, vol. 13(3), pages 887-900.
    10. Hayat D. Bedru & Chen Zhang & Feng Xie & Shuo Yu & Iftikhar Hussain, 2023. "CLARA: citation and similarity-based author ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1091-1117, February.
    11. Yonghe Lu & Meilu Yuan & Jiaxin Liu & Minghong Chen, 2023. "Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1367-1393, February.
    12. Yongquan Chen & Ying Jiang & Haiyi Liu, 2023. "Analysis Method of App Software User Experience Based on Multisource Information Fusion," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-22, January.
    13. 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.
    14. Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    15. Wei Wang & Shuo Yu & Teshome Megersa Bekele & Xiangjie Kong & Feng Xia, 2017. "Scientific collaboration patterns vary with scholars’ academic ages," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 329-343, July.
    16. Zafar Ali & Irfan Ullah & Amin Khan & Asim Ullah Jan & Khan Muhammad, 2021. "An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4083-4119, May.
    17. Alhoori, Hamed & Furuta, Richard, 2017. "Recommendation of scholarly venues based on dynamic user interests," Journal of Informetrics, Elsevier, vol. 11(2), pages 553-563.
    18. Xiaojuan Zhang & Shuqi Song & Yuping Xiong, 2024. "Personalized global citation recommendation with diversification awareness," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3625-3657, July.
    19. Hui Wang & ZiChun Le, 2021. "Expert recommendations based on link prediction during the COVID-19 outbreak," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4639-4658, June.
    20. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2023. "Context-aware citation recommendation of scientific papers: comparative study, gaps and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4243-4268, August.

    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:127:y:2022:i:5:d:10.1007_s11192-022-04354-1. 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.