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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Yi Bu, Jiaqi Lei, Jiqun Liu, Liang Hu
Journal of Data and Information Science
Research Data Management Practices
article

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

Yi Bu, Jiaqi Lei, Jiqun Liu, Liang Hu
article en

Abstract

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.

Journal of Data and Information ScienceVol. 11(1)
Peking University (CN), University of Oklahoma (US), Tsinghua University (CN)
National Science Foundation, National Natural Science Foundation of China
Openalex Percentile: Top 57%
Research Data Management Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Understanding teams and productivity in information retrieval research: Academia, industry, and cross-community collaborations — Yi Bu, Jiaqi Lei, et al. · Journal of Data and Information Science (2025) | TGRS Research Map | TGRS