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Information Retrieval Research in Academia and Industry: A Preliminary Analysis of Productivity, Authorship, Impact, and Topic Distribution

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Information for a Better World: Normality, Virtuality, Physicality, Inclusivity (iConference 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13972))

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Abstract

Due to the increasingly diversified research topics, approaches, and applications, there is a growing trend of scientific collaboration between academia and industry in information retrieval (IR) research. However, the characteristics in productivity, authorship, impact, and topics for publications by researchers from academia and industry and by academia-industry collaboration still remain understudied. In this paper, we examine the features and differences regarding productivity, authorship, and impact of the three types of studies and also pay special attention to the research problems and topics that attract and foster academia-industry collaborations in the recent two decades of IR studies. To this end, we analyzed 36,072 research papers published by 52,419 authors from 2000–2021 in the field of IR from ACM Digital Library. We find that the three categories have clear preferences in terms of selecting which academic conferences for publication. Regarding author teams, the industry community prefers small teams or solo-authored publications compared with the academic community. As for impact, papers by academia-industry collaboration tend to have higher citation impact compared with the research where only one community is involved. The thematic analysis of academia-industry collaborative papers and co-authorship network analysis reveal the preferred choice of research topics and the continuous “centrality” of researchers from academia. Knowledge from the study offers a new perspective for analyzing the advance and emerging trends in IR research and further clarifies the cross-community collaborations and scientific contributions of academia and industry.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/.

  2. 2.

    https://www.grid.ac.

  3. 3.

    https://chiir.org/.

  4. 4.

    The specific topics and subareas of collaboration are extracted and inferred from individual research papers and main authors’ research profiles available via Google Scholar and DBLP.

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Authors and Affiliations

  1. Department of Information Management, Peking University, Beijing, 100871, China

    Jiaqi Lei & Yi Bu

  2. School of Library and Information Studies, The University of Oklahoma, Norman, OK, 73019, USA

    Jiqun Liu

Authors
  1. Jiaqi Lei
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  2. Yi Bu
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  3. Jiqun Liu
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Corresponding author

Correspondence to Jiaqi Lei .

Editor information

Editors and Affiliations

  1. iSchool Organization, Berlin, Germany

    Isaac Sserwanga

  2. Victoria University of Wellington, Wellington, New Zealand

    Anne Goulding

  3. University of Missouri, Chicago, IL, USA

    Heather Moulaison-Sandy

  4. University of South Australia, Adelaide, SA, Australia

    Jia Tina Du

  5. University of Porto, Porto, Portugal

    António Lucas Soares

  6. Monash University, Clayton, VIC, Australia

    Viviane Hessami

  7. University of Tennessee at Knoxville, Knoxville, TN, USA

    Rebecca D. Frank

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Lei, J., Bu, Y., Liu, J. (2023). Information Retrieval Research in Academia and Industry: A Preliminary Analysis of Productivity, Authorship, Impact, and Topic Distribution. In: Sserwanga, I., et al. Information for a Better World: Normality, Virtuality, Physicality, Inclusivity. iConference 2023. Lecture Notes in Computer Science, vol 13972. Springer, Cham. https://doi.org/10.1007/978-3-031-28032-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-28032-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28031-3

  • Online ISBN: 978-3-031-28032-0

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