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Sections-based bibliographic coupling for research paper recommendation

Author

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  • Raja Habib

    (Capital University of Science and Technology)

  • Muhammad Tanvir Afzal

    (Capital University of Science and Technology)

Abstract

Digital libraries suffer from the problem of information overload due to immense proliferation of research papers in journals and conference papers. This makes it challenging for researchers to access the relevant research papers. Fortunately, research paper recommendation systems offer a solution to this dilemma by filtering all the available information and delivering what is most relevant to the user. Researchers have proposed numerous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, including co-citation and bibliographic coupling, have proven to be significant. Researchers have extended the co-citation approach to include content analysis and citation proximity analysis and this has led to improvement in the accuracy of recommendations. However, in co-citation analysis, similarity between papers is discovered based on the frequency of co-cited papers in different research papers that can belong to different areas. Bibliographic coupling, on the other hand, determines the relevance between two papers based on their common references. Therefore, bibliographic coupling has inherited the benefits of recommending relevant papers; however, traditional bibliographic coupling does not consider the citing patterns of common references in different logical sections of the citing papers. Since the use of citation proximity analysis in co-citation has improved the accuracy of paper recommendation, this paper proposes a paper recommendation approach that extends the traditional bibliographic coupling by exploiting the distribution of citations in logical sections in bibliographically coupled papers. Comprehensive automated evaluation utilizing Jensen Shannon Divergence was conducted to evaluate the proposed approach. The results showed significant improvement over traditional bibliographic coupling and content-based research paper recommendation.

Suggested Citation

  • Raja Habib & Muhammad Tanvir Afzal, 2019. "Sections-based bibliographic coupling for research paper recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 643-656, May.
    • Handle: RePEc:spr:scient:v:119:y:2019:i:2:d:10.1007_s11192-019-03053-8
    DOI: 10.1007/s11192-019-03053-8
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    References listed on IDEAS

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    1. Ding, Ying & Liu, Xiaozhong & Guo, Chun & Cronin, Blaise, 2013. "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, Elsevier, vol. 7(3), pages 583-592.
    2. Alison Callahan & Stephen Hockema & Gunther Eysenbach, 2010. "Contextual cocitation: Augmenting cocitation analysis and its applications," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(6), pages 1130-1143, June.
    3. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    4. Shengbo Liu & Chaomei Chen, 2012. "The proximity of co-citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 495-511, May.
    5. Hauke Jan & Kossowski Tomasz, 2011. "Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data," Quaestiones Geographicae, Sciendo, vol. 30(2), pages 87-93, June.
    6. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    7. Shengbo Liu & Chaomei Chen, 2013. "The differences between latent topics in abstracts and citation contexts of citing papers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(3), pages 627-639, March.
    8. Ying Ding & Guo Zhang & Tamy Chambers & Min Song & Xiaolong Wang & Chengxiang Zhai, 2014. "Content-based citation analysis: The next generation of citation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(9), pages 1820-1833, September.
    9. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co‐citation clustering using full text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
    10. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co-citation clustering using full text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
    11. Shengbo Liu & Chaomei Chen, 2013. "The differences between latent topics in abstracts and citation contexts of citing papers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(3), pages 627-639, March.
    12. Aaron Elkiss & Siwei Shen & Anthony Fader & Güneş Erkan & David States & Dragomir Radev, 2008. "Blind men and elephants: What do citation summaries tell us about a research article?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 51-62, January.
    13. Boyack, Kevin W. & van Eck, Nees Jan & Colavizza, Giovanni & Waltman, Ludo, 2018. "Characterizing in-text citations in scientific articles: A large-scale analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 59-73.
    14. Siniša Maričić & Jagoda Spaventi & Leo Pavičić & Greta Pifat‐Mrzljak, 1998. "Citation context versus the frequency counts of citation histories," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(6), pages 530-540.
    15. Alison Callahan & Stephen Hockema & Gunther Eysenbach, 2010. "Contextual cocitation: Augmenting cocitation analysis and its applications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(6), pages 1130-1143, June.
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