--- title: "RecSys (A): papers and universities" description: "RecSys (ACM International Conference on Recommender Systems) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 740 of its papers from 2024–2026 (217 in 2024, 239 in 2025, 284 in 2026). By the countries of the authors' institutions, the most papers come from the United States (222), China (136) and Italy (81)." url: https://scholar4ai.com/proceedings/recsys markdown_url: https://scholar4ai.com/proceedings/recsys.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "RecSys (ACM International Conference on Recommender Systems) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 740 of its papers from 2024–2026 (217 in 2024, 239 in 2025, 284 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) --- # RecSys: ACM International Conference on Recommender Systems — papers and institutions, 2024–2026 Papers of RecSys, ACM International Conference on Recommender Systems (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 - RecSys (ACM International Conference on Recommender Systems) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 740 of its papers from 2024–2026 (217 in 2024, 239 in 2025, 284 in 2026). - By the countries of the authors' institutions, the most papers come from the United States (222), China (136) and Italy (81). - The institutions with the most papers are Google (United States) (36), Meta (United States) (28) and University of Cagliari (24). - Its most-cited paper of these years is “Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models” (2024), with 156 citations. ## Papers per year *RecSys papers per publication year in OpenAlex (–: not in OpenAlex yet)* | Year | Papers | With affiliations | |---|---:|---:| | 2024 | 217 | 204 | | 2025 | 239 | 219 | | 2026 | 284 | 267 | ## Sources *Where OpenAlex lists the papers* | OpenAlex source | Which papers | |---|---| | [S7407086088](https://openalex.org/S7407086088) | The conference's proceedings | ## Who publishes there *Institutions with the most RecSys papers, 2024–2026* | # | Institution | Papers | |---:|---|---:| | 1 | Google (United States) | 36 | | 2 | Meta (United States) | 28 | | 3 | University of Cagliari | 24 | | 4 | Kuaishou (China) | 23 | | 5 | Amazon (United States) | 20 | | 6 | University of Bari Aldo Moro | 19 | | 7 | Polytechnic University of Bari | 19 | | 8 | Johannes Kepler University of Linz | 17 | | 9 | TU Wien | 16 | | 10 | National Research University Higher School of Economics | 15 | *Countries with the most RecSys papers, 2024–2026* | # | Country | Papers | |---:|---|---:| | 1 | United States | 222 | | 2 | China | 136 | | 3 | Italy | 81 | | 4 | Austria | 60 | | 5 | Netherlands | 46 | | 6 | United Kingdom | 32 | | 7 | Germany | 28 | | 8 | Russia | 24 | | 9 | Hong Kong | 21 | | 10 | Belgium | 20 | *Keywords with the most RecSys papers, 2024–2026* | # | Keyword | Papers | |---:|---|---:| | 1 | recommender systems | 302 | | 2 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 106 | | 3 | sequential recommendation | 56 | | 4 | collaborative filtering | 37 | | 5 | music recommendation | 34 | | 6 | user engagement | 33 | | 7 | personalized recommendation | 30 | | 8 | explainable recommendation | 25 | | 9 | data sparsity | 23 | | 10 | generative recommendation | 23 | ## Most-cited papers *Most-cited RecSys papers of 2024–2026 (citations counted by OpenAlex)* | # | Citations | Title | Published | Authors | |---:|---:|---|---|---| | 1 | 156 | [Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models](https://doi.org/10.1145/3640457.3688104) | 2024-10-08 | Yunjia Xi, Weiwen Liu, Jianghao Lin et al. | | 2 | 49 | [Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations](https://doi.org/10.1145/3640457.3688190) | 2024-10-08 | Anima Singh, Trung Vu, Nikhil Mehta et al. | | 3 | 39 | [From Clicks to Carbon: The Environmental Toll of Recommender Systems](https://doi.org/10.1145/3640457.3688074) | 2024-10-08 | Tobias Vente, Lukas Wegmeth, Alan Said et al. | | 4 | 35 | [Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations](https://doi.org/10.1145/3640457.3688137) | 2024-10-08 | Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata et al. | | 5 | 31 | [Large Language Models as Evaluators for Recommendation Explanations](https://doi.org/10.1145/3640457.3688075) | 2024-10-08 | Xiaoyu Zhang, Yishan Li, Jiayin Wang et al. | | 6 | 31 | [Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models](https://doi.org/10.1145/3640457.3688118) | 2024-10-08 | Yu Cui, Feng Liu, Pengbo Wang et al. | | 7 | 27 | [Scaling Law of Large Sequential Recommendation Models](https://doi.org/10.1145/3640457.3688129) | 2024-10-08 | Gaowei Zhang, Yupeng Hou, Hongyu Lu et al. | | 8 | 25 | [Towards Empathetic Conversational Recommender Systems](https://doi.org/10.1145/3640457.3688133) | 2024-10-08 | Xiaoyu Zhang, Ruobing Xie, Yougang Lyu et al. | | 9 | 25 | [A Multi-modal Modeling Framework for Cold-start Short-video Recommendation](https://doi.org/10.1145/3640457.3688098) | 2024-10-08 | Gaode Chen, Ruina Sun, Yuezihan Jiang et al. | | 10 | 24 | [CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation](https://doi.org/10.1145/3640457.3688121) | 2024-10-08 | Yaoyiran Li, Xiang Zhai, Moustafa Alzantot et al. | ## Cite this page Scholar4AI. (2026). RecSys: ACM International Conference on Recommender Systems — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/recsys ```bibtex @misc{scholar4ai-proceedings-recsys, author = {{Scholar4AI}}, title = {RecSys: ACM International Conference on Recommender Systems — papers and institutions, 2024–2026}, year = {2026}, howpublished = {\url{https://scholar4ai.com/proceedings/recsys}}, 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/).