Understanding Teams and Productivity in Information Retrieval Research (2000-2018): Academia, Industry, and Cross-Community Collaborations
Jiaqi Lei, Liang Hu, Yi Bu, Jiqun Liu

TL;DR
This study analyzes 53,471 IR research papers from 2000-2018 to understand collaboration patterns, research topics, and productivity across academia and industry, revealing increasing diversity and specialization in the field.
Contribution
It provides a comprehensive empirical analysis of IR research collaborations, topics, and productivity, highlighting differences and growth in academia-industry partnerships.
Findings
Academic, industry, and collaborative research focus on different topics.
Academia-Industry collaborations tend to involve larger teams.
The IR field has become more diverse and thematically rich over time.
Abstract
Previous researches on the Information retrieval (IR) field have focused on summarizing progress and synthesizing knowledge and techniques from individual studies and data-driven experiments, the extent of contributions and collaborations between researchers from different communities (e.g., academia and industry) in advancing IR knowledge remains unclear. To address this gap, this study explores several characteristics of information retrieval research in four areas: productivity patterns and preferred venues, the relationship between citations and downloads, changes in research topics, and changes in patterns of scientific collaboration, by analyzing 53,471 papers published between 2000 and 2018 from the Association for Computing Machinery (ACM) Digital Library dataset. Through the analysis and interpretation on empirical datasets, we find that academic research, industry research,…
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Taxonomy
TopicsBig Data and Business Intelligence · Data Quality and Management · Semantic Web and Ontologies
