---
title: "WSDM (A): papers and universities"
description: "WSDM (ACM International Conference on Web Search and Data Mining) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 541 of its papers from 2024–2026 (175 in 2024, 150 in 2025, 216 in 2026). By the countries of the authors' institutions, the most papers come from China (212), the United States (189) and Australia (36)."
url: https://scholar4ai.com/proceedings/wsdm
markdown_url: https://scholar4ai.com/proceedings/wsdm.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "WSDM (ACM International Conference on Web Search and Data Mining) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 541 of its papers from 2024–2026 (175 in 2024, 150 in 2025, 216 in 2026). (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)
---

# WSDM: ACM International Conference on Web Search and Data Mining — papers and institutions, 2024–2026

Papers of WSDM, ACM International Conference on Web Search and Data Mining (CORE rank A; field: Data management and data science, Machine learning), 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

- WSDM (ACM International Conference on Web Search and Data Mining) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 541 of its papers from 2024–2026 (175 in 2024, 150 in 2025, 216 in 2026).
- By the countries of the authors' institutions, the most papers come from China (212), the United States (189) and Australia (36).
- The institutions with the most papers are Amazon (United States) (29), University of Science and Technology of China (24) and Chinese Academy of Sciences (17).
- Its most-cited paper of these years is “LLMRec: Large Language Models with Graph Augmentation for Recommendation” (2024), with 284 citations.

## Papers per year

*WSDM papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 175 | 164 |
| 2025 | 150 | 144 |
| 2026 | 216 | 210 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4363608885](https://openalex.org/S4363608885) | The conference's proceedings |

## Who publishes there

*Institutions with the most WSDM papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | Amazon (United States) | 29 |
| 2 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 24 |
| 3 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 17 |
| 4 | Alibaba Group (China) | 17 |
| 5 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 15 |
| 6 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 15 |
| 7 | Google (United States) | 14 |
| 8 | Tencent (China) | 14 |
| 9 | Ant Group (China) | 14 |
| 10 | [Beihang University](https://scholar4ai.com/institutions/beihang-university.md) | 14 |

*Countries with the most WSDM papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 212 |
| 2 | United States | 189 |
| 3 | Australia | 36 |
| 4 | South Korea | 33 |
| 5 | Hong Kong | 31 |
| 6 | Germany | 27 |
| 7 | Singapore | 25 |
| 8 | United Kingdom | 20 |
| 9 | Canada | 19 |
| 10 | Netherlands | 16 |

*Keywords with the most WSDM papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 101 |
| 2 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 53 |
| 3 | recommender systems | 50 |
| 4 | sequential recommendation | 23 |
| 5 | [contrastive learning](https://scholar4ai.com/keywords/contrastive-learning.md) | 17 |
| 6 | knowledge graphs | 17 |
| 7 | retrieval-augmented generation | 16 |
| 8 | node classification | 15 |
| 9 | personalized recommendation | 15 |
| 10 | causal inference | 14 |

## Most-cited papers

*Most-cited WSDM papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 284 | [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://doi.org/10.1145/3616855.3635853) | 2024-03-04 | Wei Wei, Xubin Ren, Jiabin Tang et al. |
| 2 | 161 | [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://doi.org/10.1145/3616855.3635850) | 2024-03-04 | Yangqin Jiang, Yuhao Yang, Lianghao Xia et al. |
| 3 | 155 | [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://doi.org/10.1145/3616855.3635752) | 2024-03-04 | Yuan Sui, Mengyu Zhou, Mingjie Zhou et al. |
| 4 | 124 | [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://doi.org/10.1145/3616855.3635845) | 2024-03-04 | Qijiong Liu, Nuo Chen, Tetsuya Sakai et al. |
| 5 | 108 | [GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction](https://doi.org/10.1145/3616855.3635767) | 2024-03-04 | Amit Roy, Juan Shu, Jia Li et al. |
| 6 | 96 | [Linear Recurrent Units for Sequential Recommendation](https://doi.org/10.1145/3616855.3635760) | 2024-03-04 | Zhenrui Yue, Y. Wang, Zhankui He et al. |
| 7 | 88 | [K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization](https://doi.org/10.1145/3616855.3635772) | 2024-03-04 | Cheng Deng, Tianhang Zhang, Zhongmou He et al. |
| 8 | 73 | [Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation](https://doi.org/10.1145/3616855.3635773) | 2024-03-04 | Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao et al. |
| 9 | 72 | [Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions](https://doi.org/10.1145/3616855.3635856) | 2024-03-04 | Zahra Abbasiantaeb, Yifei Yuan, Evangelos Kanoulas et al. |
| 10 | 70 | [HealAI: A Healthcare LLM for Effective Medical Documentation](https://doi.org/10.1145/3616855.3635739) | 2024-03-04 | Sagar Goyal, Eti Rastogi, Sree Prasanna Rajagopal et al. |

## Cite this page

Scholar4AI. (2026). WSDM: ACM International Conference on Web Search and Data Mining — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/wsdm

```bibtex
@misc{scholar4ai-proceedings-wsdm,
  author = {{Scholar4AI}},
  title = {WSDM: ACM International Conference on Web Search and Data Mining — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/wsdm}},
  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/).