IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v102y2015i2d10.1007_s11192-014-1467-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-014-1467-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-014-1467-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongjun Zhu & Erjia Yan, 2015. "Dynamic subfield analysis of disciplines: an examination of the trading impact and knowledge diffusion patterns of computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 335-359, July.
    2. Ruhua Huang & Yuting Huang & Fan Qi & Leyi Shi & Baiyang Li & Wei Yu, 2022. "Exploring the characteristics of special issues: distribution, topicality, and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5233-5256, September.
    3. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.
    4. Carlos Olmeda-Gómez & Maria-Antonia Ovalle-Perandones & Antonio Perianes-Rodríguez, 2017. "Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 195-217, October.
    5. Xiaoguang Wang & Hongyu Wang & Han Huang, 2021. "Evolutionary exploration and comparative analysis of the research topic networks in information disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4991-5017, June.
    6. Cristina Gomes Souza & Marta Lúcia Azevedo Ferreira, 2013. "Researchers profile, co-authorship pattern and knowledge organization in information science in Brazil," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 673-687, May.
    7. Luis Miguel Pérez & Raul Oltra-Badenes & Juan Vicente Oltra Gutiérrez & Hermenegildo Gil-Gómez, 2020. "A Bibliometric Diagnosis and Analysis about Smart Cities," Sustainability, MDPI, vol. 12(16), pages 1-43, August.
    8. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    9. Alberto Martín-Martín & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2018. "A novel method for depicting academic disciplines through Google Scholar Citations: The case of Bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1251-1273, March.
    10. Danielle H. Lee, 2019. "Predicting the research performance of early career scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1481-1504, December.
    11. Yuen-Hsien Tseng & Ming-Yueh Tsay, 2013. "Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 503-528, May.
    12. Danielle H. Lee, 2019. "Predictive power of conference-related factors on citation rates of conference papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 281-304, January.
    13. Yan, Erjia & Ding, Ying & Cronin, Blaise & Leydesdorff, Loet, 2013. "A bird's-eye view of scientific trading: Dependency relations among fields of science," Journal of Informetrics, Elsevier, vol. 7(2), pages 249-264.
    14. Mu-hsuan Huang & Wang-Ching Shaw & Chi-Shiou Lin, 2019. "One category, two communities: subfield differences in “Information Science and Library Science” in Journal Citation Reports," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1059-1079, May.
    15. William H. Walters & Esther Isabelle Wilder, 2015. "Worldwide contributors to the literature of library and information science: top authors, 2007–2012," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 301-327, April.
    16. Sümeyye Akça & Özlem Şenyurt, 2023. "Geographical representation of editorial boards: a review in the field of library and information sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1409-1427, February.
    17. Maja Jokić, 2020. "Productivity, visibility, authorship, and collaboration in library and information science journals: Central and Eastern European authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1189-1219, February.
    18. Min Song & Go Eun Heo & Su Yeon Kim, 2014. "Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 397-428, October.
    19. Jingjing Ren & Fang Wang & Minglu Li, 2023. "Dynamics and characteristics of interdisciplinary research in scientific breakthroughs: case studies of Nobel-winning research in the past 120 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4383-4419, August.
    20. Aurora González-Teruel & Gregorio González-Alcaide & Maite Barrios & María-Francisca Abad-García, 2015. "Mapping recent information behavior research: an analysis of co-authorship and co-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 687-705, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:102:y:2015:i:2:d:10.1007_s11192-014-1467-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.