Understanding teams and productivity in information retrieval research: Academia, industry, and cross-community collaborations

Jiaqi Lei, Liang Hu, Yi Bu, Jiqun Liu

doi:10.2478/jdis-2025-0051 CSTR: 32295.14.jdis-2025-0051

Journal of Data and Information Science >

0 20251013 - 20251013

DOI: https://doi.org/10.2478/jdis-2025-0051 CSTR: 32295.14.jdis-2025-0051

Research Papers

Understanding teams and productivity in information retrieval research: Academia, industry, and cross-community collaborations

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¹Institute of Education, Tsinghua University, Beijing 100084, China;
²Department of Information Management, Peking University, Beijing 100871, China;
³School of Library and Information Studies, The University of Oklahoma, Norman, Oklahoma 73019, U.S.A.

† Yi Bu (Email: buyi@pku.edu.cn); Jiqun Liu (Email: jiqunliu@ou.edu).

* These authors contributed to this paper equally.

Received date: 2025-06-06

Revised date: 2025-08-07

Accepted date: 2025-09-19

Online published: 2025-10-13

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Abstract

Purpose: Prior Information Retrieval (IR) research synthesizes progress from individual studies, yet academia-industry collaboration dynamics remain unexplored. This study investigates: (1) productivity patterns and venues, (2) citations-downloads relationships, (3) topic evolution, and (4) collaboration trends.
PDesign/methodology/approach: We perform an analysis of 53,471 ACM IR papers (2000-2018) using bibliometrics and DistilBERT topic modeling.
Findings: We find that industry-involved papers preferred WWW/CIKM venues; collaborations dominated RecSys/CSCW. We see that academia-industry collaborations achieved the highest download-to-citation conversion rates. Academia focused on algorithms; industry on applications; collaborations bridged both with rising human-centered themes.
Research implications: This is a pioneering large-scale bibliometrics revealing collaboration’s impact on IR knowledge evolution and provides a methodological framework for cross-sector analysis.
Practical implications: The paper identifies optimal venues (RecSys/CSCW) for partnerships and guides joint initiatives (shared datasets, grants) to bridge academia-industry divides and enhance research translation.
Originality/value: This is the first large-scale bibliometric analysis of IR academia-industry collaboration. The paper finds many novel insights, including the fact that collaboration boosts citation efficiency, enables complementary specialization, and drives topic convergence.

Key words： Information Retrieval; Bibliometrics; Productivity; Research topic; Scientific collaboration; Academic-industry collaboration

Cite this article

Jiaqi Lei, Liang Hu, Yi Bu, Jiqun Liu . Understanding teams and productivity in information retrieval research: Academia, industry, and cross-community collaborations[J]. Journal of Data and Information Science, 0 : 20251013 -20251013 . DOI: 10.2478/jdis-2025-0051 CSTR: 32295.14.jdis-2025-0051

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