Research paper recommender system based on public contextual metadata

Khalid Haruna (), Maizatul Akmar Ismail (), Atika Qazi, Habeebah Adamu Kakudi, Mohammed Hassan, Sanah Abdullahi Muaz and Haruna Chiroma ()
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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

Scientometrics, 2020, vol. 125, issue 1, No 4, 114 pages

Abstract: 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.

Keywords: Research paper recommendation framework; Paper-citation relations; Priori user profile; Public contextual metadata (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-020-03642-y

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