Understanding teams and productivity in information retrieval research: Academia, industry, and cross-community collaborations
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. Design/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.
Authors
- Yi Bu (ORCID: https://orcid.org/0000-0003-2549-4580)
- Jiaqi Lei (ORCID: https://orcid.org/0000-0002-2695-6444)
- Jiqun Liu (ORCID: https://orcid.org/0000-0003-3643-2182)
- Liang Hu (ORCID: https://orcid.org/0000-0003-3670-7596)
Institutions
- Peking University (CN)
- University of Oklahoma (US)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of Data and Information Science
- Published
- 2025-10-16
- DOI
- https://doi.org/10.2478/jdis-2025-0051
- Primary Topic
- Research Data Management Practices
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Natural Science Foundation of China