SIGIR: ACM International Conference on Research and Development in Information Retrieval — papers and institutions, 2024–2026

Papers of SIGIR, ACM International Conference on Research and Development in Information Retrieval (CORE rank A*; field: Data management and data science), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02. Also as Markdown.

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Key findings#

SIGIR (ACM International Conference on Research and Development in Information Retrieval) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,614 of its papers from 2024–2026 (389 in 2024, 540 in 2025, 685 in 2026).
By the countries of the authors' institutions, the most papers come from China (723), the United States (350) and Australia (140).
The institutions with the most papers are University of Amsterdam (84), Chinese Academy of Sciences (68) and Renmin University of China (68).
Its most-cited paper of these years is “C-Pack: Packed Resources For General Chinese Embeddings” (2024), with 516 citations.

Papers per year#

SIGIR papers per publication year in OpenAlex (–: not in OpenAlex yet)
Year	Papers	With affiliations
2024	389	381
2025	540	530
2026	685	650

Sources#

Where OpenAlex lists the papers
OpenAlex source	Which papers
S4363608773	The conference's proceedings

Who publishes there#

Institutions with the most SIGIR papers, 2024–2026
#	Institution	Papers
1	University of Amsterdam	84
2	Chinese Academy of Sciences	68
3	Renmin University of China	68
4	Tsinghua University	64
5	University of Science and Technology of China	58
6	The University of Queensland	50
7	National University of Singapore	49
8	University of Chinese Academy of Sciences	49
9	University of Glasgow	49
10	Alibaba Group (China)	45

Countries with the most SIGIR papers, 2024–2026
#	Country	Papers
1	China	723
2	United States	350
3	Australia	140
4	Netherlands	122
5	United Kingdom	108
6	Hong Kong	87
7	Singapore	85
8	Canada	81
9	Germany	81
10	Italy	71

Keywords with the most SIGIR papers, 2024–2026
#	Keyword	Papers
1	large language models	436
2	recommender systems	171
3	retrieval-augmented generation	157
4	information retrieval	93
5	sequential recommendation	67
6	dense retrieval	60
7	contrastive learning	57
8	reproducibility	51
9	graph neural networks	46
10	knowledge distillation	46

Most-cited papers#

Most-cited SIGIR papers of 2024–2026 (citations counted by OpenAlex)
#	Citations	Title	Published	Authors
1	516	C-Pack: Packed Resources For General Chinese Embeddings	2024-07-10	Shitao Xiao, Zheng Liu, Peitian Zhang et al.
2	237	The Power of Noise: Redefining Retrieval for RAG Systems	2024-07-10	Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al.
3	207	GraphGPT: Graph Instruction Tuning for Large Language Models	2024-07-10	Jiabin Tang, Yuhao Yang, Wei Wei et al.
4	170	Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering	2024-07-10	Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al.
5	169	Evaluating Retrieval Quality in Retrieval-Augmented Generation	2024-07-10	Alireza Salemi, Hamed Zamani
6	168	Large Language Models can Accurately Predict Searcher Preferences	2024-07-10	Paul Thomas, Seth E. Spielman, Nick Craswell et al.
7	165	Data-efficient Fine-tuning for LLM-based Recommendation	2024-07-10	Xinyu Lin, Wenjie Wang, Yongqi Li et al.
8	132	LLaRA: Large Language-Recommendation Assistant	2024-07-10	Jiayi Liao, Sihang Li, Zhengyi Yang et al.
9	127	Fine-Tuning LLaMA for Multi-Stage Text Retrieval	2024-07-10	Xueguang Ma, Liang Wang, Nan Yang et al.
10	119	On Generative Agents in Recommendation	2024-07-10	An Zhang, Yuxin Chen, Leheng Sheng et al.

Conferences in the same field#

KDD · WWW · VLDB · ICDE · SIGMOD · ICDM · PODS · CIKM · ECML PKDD · RecSys · ECIR · WSDM · ICWSM · ICDAR · MSR · SIGSPATIAL · ISWC · SDM · CAiSE · ICDT · ER

Cite this page

Scholar4AI. (2026). SIGIR: ACM International Conference on Research and Development in Information Retrieval — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/sigir

@misc{scholar4ai-proceedings-sigir,
  author = {{Scholar4AI}},
  title = {SIGIR: ACM International Conference on Research and Development in Information Retrieval — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/sigir}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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