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Research paper recommender system based on public contextual metadata

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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.

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Acknowledgements

This study was supported by the Tertiary Education Trust Fund (TETFund) Institutional Based Research (IBR) Fund, through the Directorate of Research, Innovation and Partnership (DRIP) of Bayero University, Kano, Nigeria (2019), and partly supported by University of Malaya under research grant IIRG001B-19SAH.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Computer Science and Information Technology, Bayero University, Kano, Nigeria

    Khalid Haruna & Habeebah Adamu Kakudi

  2. Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Maizatul Akmar Ismail

  3. Centre for Lifelong Learning, Universiti Brunei Darussalam, Gadong, BE1410, Brunei

    Atika Qazi

  4. Department of Software Engineering, Faculty of Computer Science and Information Technology, Bayero University, Kano, Nigeria

    Mohammed Hassan & Sanah Abdullahi Muaz

  5. Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Yunlin, Taiwan

    Haruna Chiroma

Authors
  1. Khalid Haruna
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  2. Maizatul Akmar Ismail
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  3. Atika Qazi
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  4. Habeebah Adamu Kakudi
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  5. Mohammed Hassan
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  6. Sanah Abdullahi Muaz
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  7. Haruna Chiroma
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Correspondence to Khalid Haruna, Maizatul Akmar Ismail or Haruna Chiroma.

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Haruna, K., Ismail, M.A., Qazi, A. et al. Research paper recommender system based on public contextual metadata. Scientometrics 125, 101–114 (2020). https://doi.org/10.1007/s11192-020-03642-y

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  • DOI: https://doi.org/10.1007/s11192-020-03642-y

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