---
title: "SIGIR (A*): papers and universities"
description: "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)."
url: https://scholar4ai.com/proceedings/sigir
markdown_url: https://scholar4ai.com/proceedings/sigir.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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… (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# 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.

All conferences: https://scholar4ai.com/proceedings.md

## 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](https://openalex.org/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](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 68 |
| 3 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | 68 |
| 4 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 64 |
| 5 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 58 |
| 6 | The University of Queensland | 50 |
| 7 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 49 |
| 8 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 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](https://scholar4ai.com/keywords/large-language-models.md) | 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](https://scholar4ai.com/keywords/contrastive-learning.md) | 57 |
| 8 | reproducibility | 51 |
| 9 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 46 |
| 10 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 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](https://doi.org/10.1145/3626772.3657878) | 2024-07-10 | Shitao Xiao, Zheng Liu, Peitian Zhang et al. |
| 2 | 237 | [The Power of Noise: Redefining Retrieval for RAG Systems](https://doi.org/10.1145/3626772.3657834) | 2024-07-10 | Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al. |
| 3 | 207 | [GraphGPT: Graph Instruction Tuning for Large Language Models](https://doi.org/10.1145/3626772.3657775) | 2024-07-10 | Jiabin Tang, Yuhao Yang, Wei Wei et al. |
| 4 | 170 | [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://doi.org/10.1145/3626772.3661370) | 2024-07-10 | Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al. |
| 5 | 169 | [Evaluating Retrieval Quality in Retrieval-Augmented Generation](https://doi.org/10.1145/3626772.3657957) | 2024-07-10 | Alireza Salemi, Hamed Zamani |
| 6 | 168 | [Large Language Models can Accurately Predict Searcher Preferences](https://doi.org/10.1145/3626772.3657707) | 2024-07-10 | Paul Thomas, Seth E. Spielman, Nick Craswell et al. |
| 7 | 165 | [Data-efficient Fine-tuning for LLM-based Recommendation](https://doi.org/10.1145/3626772.3657807) | 2024-07-10 | Xinyu Lin, Wenjie Wang, Yongqi Li et al. |
| 8 | 132 | [LLaRA: Large Language-Recommendation Assistant](https://doi.org/10.1145/3626772.3657690) | 2024-07-10 | Jiayi Liao, Sihang Li, Zhengyi Yang et al. |
| 9 | 127 | [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://doi.org/10.1145/3626772.3657951) | 2024-07-10 | Xueguang Ma, Liang Wang, Nan Yang et al. |
| 10 | 119 | [On Generative Agents in Recommendation](https://doi.org/10.1145/3626772.3657844) | 2024-07-10 | An Zhang, Yuxin Chen, Leheng Sheng et al. |

## 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).