Understanding teams and productivity in information retrieval research: Academia, industry, and cross-community collaborations
Year: 2025
Type: article
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.
Authors: Jiaqi Lei, Liang Hu, Yi Bu, Jiqun Liu
Institutions: Peking University, Tsinghua University, University of Oklahoma
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
Awards: G3535887529, G7131482003
OpenAlex ID: W4415244229