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Research paper recommender system based on public contextual metadata

Author

Listed:
  • Khalid Haruna

    (Bayero University)

  • Maizatul Akmar Ismail

    (University of Malaya)

  • Atika Qazi

    (Universiti Brunei Darussalam)

  • Habeebah Adamu Kakudi

    (Bayero University)

  • Mohammed Hassan

    (Bayero University)

  • Sanah Abdullahi Muaz

    (Bayero University)

  • Haruna Chiroma

    (National Yunlin University of Science and Technology)

Abstract

Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods.

Suggested Citation

  • Khalid Haruna & Maizatul Akmar Ismail & Atika Qazi & Habeebah Adamu Kakudi & Mohammed Hassan & Sanah Abdullahi Muaz & Haruna Chiroma, 2020. "Research paper recommender system based on public contextual metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 101-114, October.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03642-y
    DOI: 10.1007/s11192-020-03642-y
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    References listed on IDEAS

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    1. Khalid Haruna & Maizatul Akmar Ismail & Damiasih Damiasih & Joko Sutopo & Tutut Herawan, 2017. "A collaborative approach for research paper recommender system," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-17, October.
    2. Chanwoo Jeong & Sion Jang & Eunjeong Park & Sungchul Choi, 2020. "A context-aware citation recommendation model with BERT and graph convolutional networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1907-1922, September.
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    Cited by:

    1. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
    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.

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