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. Also as Markdown.

← All conferences

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	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	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	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	2024-10-08	Anima Singh, Trung Vu, Nikhil Mehta et al.
3	39	From Clicks to Carbon: The Environmental Toll of Recommender Systems	2024-10-08	Tobias Vente, Lukas Wegmeth, Alan Said et al.
4	35	Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations	2024-10-08	Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata et al.
5	31	Large Language Models as Evaluators for Recommendation Explanations	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	2024-10-08	Yu Cui, Feng Liu, Pengbo Wang et al.
7	27	Scaling Law of Large Sequential Recommendation Models	2024-10-08	Gaowei Zhang, Yupeng Hou, Hongyu Lu et al.
8	25	Towards Empathetic Conversational Recommender Systems	2024-10-08	Xiaoyu Zhang, Ruobing Xie, Yougang Lyu et al.
9	25	A Multi-modal Modeling Framework for Cold-start Short-video Recommendation	2024-10-08	Gaode Chen, Ruina Sun, Yuezihan Jiang et al.
10	24	CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation	2024-10-08	Yaoyiran Li, Xiang Zhai, Moustafa Alzantot et al.

Conferences in the same field#

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

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

@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}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).