@misc{lei_understanding_2024,
	title = {Understanding {Teams} and {Productivity} in {Information} {Retrieval} {Research} (2000-2018): {Academia}, {Industry}, and {Cross}-{Community} {Collaborations}},
	shorttitle = {Understanding {Teams} and {Productivity} in {Information} {Retrieval} {Research} (2000-2018)},
	url = {http://arxiv.org/abs/2410.01541},
	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, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrieval has become richer over time in terms of themes, foci, and sub-themes, becoming a more diverse field of study.},
	urldate = {2024-10-18},
	publisher = {arXiv},
	author = {Lei, Jiaqi and Hu, Liang and Bu, Yi and Liu, Jiqun},
	month = oct,
	year = {2024},
	note = {arXiv:2410.01541},
	keywords = {Computer Science - Digital Libraries},
}

{"_id":"sq2iweBYCSP5M3byQ","bibbaseid":"lei-hu-bu-liu-understandingteamsandproductivityininformationretrievalresearch20002018academiaindustryandcrosscommunitycollaborations-2024","author_short":["Lei, J.","Hu, L.","Bu, Y.","Liu, J."],"bibdata":{"bibtype":"misc","type":"misc","title":"Understanding Teams and Productivity in Information Retrieval Research (2000-2018): Academia, Industry, and Cross-Community Collaborations","shorttitle":"Understanding Teams and Productivity in Information Retrieval Research (2000-2018)","url":"http://arxiv.org/abs/2410.01541","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, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrieval has become richer over time in terms of themes, foci, and sub-themes, becoming a more diverse field of study.","urldate":"2024-10-18","publisher":"arXiv","author":[{"propositions":[],"lastnames":["Lei"],"firstnames":["Jiaqi"],"suffixes":[]},{"propositions":[],"lastnames":["Hu"],"firstnames":["Liang"],"suffixes":[]},{"propositions":[],"lastnames":["Bu"],"firstnames":["Yi"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Jiqun"],"suffixes":[]}],"month":"October","year":"2024","note":"arXiv:2410.01541","keywords":"Computer Science - Digital Libraries","bibtex":"@misc{lei_understanding_2024,\n\ttitle = {Understanding {Teams} and {Productivity} in {Information} {Retrieval} {Research} (2000-2018): {Academia}, {Industry}, and {Cross}-{Community} {Collaborations}},\n\tshorttitle = {Understanding {Teams} and {Productivity} in {Information} {Retrieval} {Research} (2000-2018)},\n\turl = {http://arxiv.org/abs/2410.01541},\n\tabstract = {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, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrieval has become richer over time in terms of themes, foci, and sub-themes, becoming a more diverse field of study.},\n\turldate = {2024-10-18},\n\tpublisher = {arXiv},\n\tauthor = {Lei, Jiaqi and Hu, Liang and Bu, Yi and Liu, Jiqun},\n\tmonth = oct,\n\tyear = {2024},\n\tnote = {arXiv:2410.01541},\n\tkeywords = {Computer Science - Digital Libraries},\n}\n\n","author_short":["Lei, J.","Hu, L.","Bu, Y.","Liu, J."],"key":"lei_understanding_2024","id":"lei_understanding_2024","bibbaseid":"lei-hu-bu-liu-understandingteamsandproductivityininformationretrievalresearch20002018academiaindustryandcrosscommunitycollaborations-2024","role":"author","urls":{"Paper":"http://arxiv.org/abs/2410.01541"},"keyword":["Computer Science - Digital Libraries"],"metadata":{"authorlinks":{}}},"bibtype":"misc","biburl":"https://api.zotero.org/groups/4790165/items?key=qWYUkNg8G2tSrs1m5i7SsKOn&format=bibtex&limit=100","dataSources":["txmtuJDjhqHfaZE3C","wkZmECJAmJTTcjXCL","ttiB3rxTuWH3fiHv3"],"keywords":["computer science - digital libraries"],"search_terms":["understanding","teams","productivity","information","retrieval","research","2000","2018","academia","industry","cross","community","collaborations","lei","hu","bu","liu"],"title":"Understanding Teams and Productivity in Information Retrieval Research (2000-2018): Academia, Industry, and Cross-Community Collaborations","year":2024}