A collaborative approach for research paper recommender system

Khalid Haruna, Maizatul Akmar Ismail, Damiasih Damiasih, Joko Sutopo and Tutut Herawan

PLOS ONE, 2017, vol. 12, issue 10, 1-17

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.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184516 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 84516&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0184516

DOI: 10.1371/journal.pone.0184516

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().