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Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles

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  • Yadav, Pratyush
  • Pervin, Nargis

Abstract

The growing popularity of digital libraries as a medium for communicating scientific discoveries has made a large variety of research articles easily accessible. However, this constitutes a putative issue of information overloading with recommendation engines providing a compelling solution to the problem. Scientific Recommender Systems alleviate this problem by suggesting potential papers of interest to a user. For any researcher seeking developments in their field, it is important that the recommended papers are of high quality, recent and related to the field of interest, which has been largely overlooked in prior approaches. This study thus proposes a graph-based hybrid recommendation technique, SPACE-R, that amalgamates quality, semantic similarity and community structure of nodes in a citation network. The creation of a popularity network, a derivative of a citation network, in combination with a two-stage candidate selection process involving community detection and neighbourhood network identification, contributes to an improvement in the accuracy and scalability of the proposed model. The incorporation of semantic similarity achieves the necessary diversity in recommendations. Experimental evaluations on four large datasets against five baselines reveal that SPACE-R achieves an improvement of up to 45.53%, 56.76%, 49.39%, 46.84% and 78.18% in recall, precision, MRR, mAP, and response time, respectively.

Suggested Citation

  • Yadav, Pratyush & Pervin, Nargis, 2022. "Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles," Journal of Informetrics, Elsevier, vol. 16(4).
  • Handle: RePEc:eee:infome:v:16:y:2022:i:4:s175115772200089x
    DOI: 10.1016/j.joi.2022.101336
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    References listed on IDEAS

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    1. Esther Landhuis, 2016. "Scientific literature: Information overload," Nature, Nature, vol. 535(7612), pages 457-458, July.
    2. Zeng, Tong & Wu, Longfeng & Bratt, Sarah & Acuna, Daniel E., 2020. "Assigning credit to scientific datasets using article citation networks," Journal of Informetrics, Elsevier, vol. 14(2).
    3. 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.
    4. Kong, Xiangjie & Mao, Mengyi & Jiang, Huizhen & Yu, Shuo & Wan, Liangtian, 2019. "How does collaboration affect researchers’ positions in co-authorship networks?," Journal of Informetrics, Elsevier, vol. 13(3), pages 887-900.
    5. Shen, Si & Zhu, Danhao & Rousseau, Ronald & Su, Xinning & Wang, Dongbo, 2019. "A refined method for computing bibliographic coupling strengths," Journal of Informetrics, Elsevier, vol. 13(2), pages 605-615.
    6. Natsuo Onodera & Fuyuki Yoshikane, 2015. "Factors affecting citation rates of research articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(4), pages 739-764, April.
    7. Aitor Gonzalez-Agirre & German Rigau & Eneko Agirre & Nikolaos Aletras & Mark Stevenson, 2016. "Why are these similar? Investigating item similarity types in a large digital library," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(7), pages 1624-1638, July.
    8. Alhoori, Hamed & Furuta, Richard, 2017. "Recommendation of scholarly venues based on dynamic user interests," Journal of Informetrics, Elsevier, vol. 11(2), pages 553-563.
    9. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    10. Ying Ding & Erjia Yan & Arthur Frazho & James Caverlee, 2009. "PageRank for ranking authors in co‐citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2229-2243, November.
    11. 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.
    12. Shutian Ma & Chengzhi Zhang & Xiaozhong Liu, 2020. "A review of citation recommendation: from textual content to enriched context," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1445-1472, March.
    13. Behrouzi, Saman & Shafaeipour Sarmoor, Zahra & Hajsadeghi, Khosrow & Kavousi, Kaveh, 2020. "Predicting scientific research trends based on link prediction in keyword networks," Journal of Informetrics, Elsevier, vol. 14(4).
    14. Iman Tahamtan & Askar Safipour Afshar & Khadijeh Ahamdzadeh, 2016. "Factors affecting number of citations: a comprehensive review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1195-1225, June.
    15. Vanclay, Jerome K., 2013. "Factors affecting citation rates in environmental science," Journal of Informetrics, Elsevier, vol. 7(2), pages 265-271.
    16. Xiaoye Cheng & Jingjing Zhang & Lu (Lucy) Yan, 2020. "Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 303-320, April.
    17. T. Liskiewicz & G. Liskiewicz & J. Paczesny, 2021. "Factors affecting the citations of papers in tribology journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3321-3336, April.
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