Affiliations 

  • 1 Department of Computer Science, Faculty of Computer Science and Information Technology, Bayero University, Kano, Nigeria
  • 2 Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
  • 3 Sekolah Tinggi Pariwisata Ambarrukmo, Yogyakarta, Indonesia
  • 4 Faculty of Information Technology and Business, Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia
PLoS One, 2017;12(10):e0184516.
PMID: 28981512 DOI: 10.1371/journal.pone.0184516

Abstract

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.