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Bibliometric study of the scientific research on “Learning to Rank” between 2000 and 2013

Author

Listed:
  • Oscar J. Alejo-Machado

    (University of Cienfuegos)

  • Juan Manuel Fernández-Luna

    (Universidad de Granada)

  • Juan F. Huete

    (Universidad de Granada)

Abstract

The application of machine learning algorithms in the construction of ranking models is a relatively new research area which has emerged during the last 10 years within the field of artificial intelligence and information retrieval. This paper presents a bibliometric study of scientific output on learning to rank (L2R) between 2000 and 2013. For this procedure to be successful, every relevant bibliographic L2R record retrieved from the Scopus database was considered. The records were processed according to a series of one-dimensional and multi-dimensional metric indicators which were selected for the study. The results of this research provide the scientific community with reliable, up-to-date information about the state of L2R research and trends, and will enable researchers to develop valuable studies to reinforce research, development and innovation.

Suggested Citation

  • Oscar J. Alejo-Machado & Juan Manuel Fernández-Luna & Juan F. Huete, 2015. "Bibliometric study of the scientific research on “Learning to Rank” between 2000 and 2013," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1669-1686, February.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:2:d:10.1007_s11192-014-1467-4
    DOI: 10.1007/s11192-014-1467-4
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    References listed on IDEAS

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    1. Yuan Lin & Hongfei Lin & Kan Xu & Xiaoling Sun, 2013. "Learning to rank using smoothing methods for language modeling," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(4), pages 818-828, April.
    2. Robert Braam & Peter Besselaar, 2014. "Indicators for the dynamics of research organizations: a biomedical case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 949-971, June.
    3. Yuan Lin & Hongfei Lin & Kan Xu & Xiaoling Sun, 2013. "Learning to rank using smoothing methods for language modeling," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(4), pages 818-828, April.
    4. Vincent Larivière & Cassidy R. Sugimoto & Blaise Cronin, 2012. "A bibliometric chronicling of library and information science's first hundred years," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(5), pages 997-1016, May.
    5. Vincent Larivière & Cassidy R. Sugimoto & Blaise Cronin, 2012. "A bibliometric chronicling of library and information science's first hundred years," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(5), pages 997-1016, May.
    6. Jiang Tan & Hui-Zhen Fu & Yuh-Shan Ho, 2014. "A bibliometric analysis of research on proteomics in Science Citation Index Expanded," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1473-1490, February.
    7. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
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    Cited by:

    1. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.

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