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Contents and time sensitive document ranking of scientific literature

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  • Xu, Han
  • Martin, Eric
  • Mahidadia, Ashesh

Abstract

A new link-based document ranking framework is devised with at its heart, a contents and time sensitive random literature explorer designed to more accurately model the behaviour of readers of scientific documents. In particular, our ranking framework dynamically adjusts its random walk parameters according to both contents and age of encountered documents, thus incorporating the diversity of topics and how they evolve over time into the score of a scientific publication. Our random walk framework results in a ranking of scientific documents which is shown to be more effective in facilitating literature exploration than PageRank measured against a proxy gold standard based on papers’ potential usefulness in facilitating later research. One of its many strengths lies in its practical value in reliably retrieving and placing promisingly useful papers at the top of its ranking.

Suggested Citation

  • Xu, Han & Martin, Eric & Mahidadia, Ashesh, 2014. "Contents and time sensitive document ranking of scientific literature," Journal of Informetrics, Elsevier, vol. 8(3), pages 546-561.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:3:p:546-561
    DOI: 10.1016/j.joi.2014.04.006
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    References listed on IDEAS

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    1. Susan Bonzi, 1982. "Characteristics of a Literature as Predictors of Relatedness Between Cited and Citing Works," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 33(4), pages 208-216, July.
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    Citations

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    Cited by:

    1. Fang Zhang & Shengli Wu, 2021. "Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7197-7222, August.
    2. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.
    3. Jun Zhang & Zhaolong Ning & Xiaomei Bai & Xiangjie Kong & Jinmeng Zhou & Feng Xia, 2017. "Exploring time factors in measuring the scientific impact of scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1301-1321, September.
    4. Antonia Ferrer-Sapena & Susana Díaz-Novillo & Enrique A. Sánchez-Pérez, 2017. "Measuring Time-Dynamics and Time-Stability of Journal Rankings in Mathematics and Physics by Means of Fractional p -Variations," Publications, MDPI, vol. 5(3), pages 1-14, September.
    5. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.
    6. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    7. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2020. "Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2637-2666, December.
    8. A. Ferrer-Sapena & J. M. Calabuig & L. M. García Raffi & E. A. Sánchez Pérez, 2020. "Where Should I Submit My Work for Publication? An Asymmetrical Classification Model to Optimize Choice," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 490-508, July.

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