MovieLens

data category: Online reviews, e-commerce and recommender data · 0 papers in the last 7 days

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Product and service reviews, ratings, purchases and user-item interactions.

Names searched for: MovieLens

Key findings#

From Nov 2023 to Oct 2026, 126 archived papers mentioned MovieLens in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jan 2026 (7).
MovieLens was mentioned in 4 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #79 of the 246 datasets mentioned.
By journal category: AI (3) and Statistics (1).
Most of them appeared in Applied Intelligence (1), Artificial Intelligence Review (1) and Statistics and Computing (1).
From 2017 to 2026 (so far), MovieLens was mentioned in 182 papers in the tracked journals, most of them in AI (146), information systems (12) and operations research (11).
33 of them were published in 2025, against 9 in 2017.
Since 2023, the institutions with the most of these papers were National University of Singapore (8), University of Science and Technology of China (7) and Zhejiang University (5).
The journals publishing most of them were IEEE Transactions on Knowledge and Data Engineering (21), Knowledge-Based Systems (10) and Artificial Intelligence Review (9).
The most-cited of them is “Recommendation system based on deep learning methods: a systematic review and new directions” (Artificial Intelligence Review, 2019), with 405 citations.
Papers use MovieLens most often with Yelp (25), Location and social recommendation data (Gowalla, Foursquare, Yelp, Epinions) (22) and Amazon reviews (20); it has 23 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	42	38	46	126

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	8	6	7	5	9	5	16	32	25	33
Information Systems	1	0	0	0	2	1	2	2	2	2
Operations Research	0	0	1	0	0	0	0	4	4	2
Economics	0	0	0	0	0	0	0	2	1	3
Statistics	0	0	0	0	0	0	0	2	1	2
Management	0	0	0	0	0	0	0	1	0	1
All categories	9	6	8	5	11	6	18	43	33	43

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	IEEE Transactions on Knowledge and Data Engineering	21
2	Knowledge-Based Systems	10
3	Artificial Intelligence Review	9
4	IEEE Transactions on Pattern Analysis and Machine Intelligence	8
5	Expert Systems with Applications	8
6	Machine Learning	6
7	IEEE Transactions on Neural Networks and Learning Systems	6
8	Mathematical Programming	5
9	Knowledge and Information Systems	5
10	Neural Computing and Applications	5

Institutions with the most papers, since 2023
#	Institution	Papers
1	National University of Singapore	8
2	University of Science and Technology of China	7
3	Zhejiang University	5
4	Chinese Academy of Sciences	4
5	National University of Defense Technology	4
6	The University of Sydney	4
7	University of Chinese Academy of Sciences	4
8	Alibaba Group (China)	3
9	Beihang University	3
10	Carnegie Mellon University	3

Countries with the most papers, since 2023
#	Country	Papers
1	China	51
2	United States	17
3	India	13
4	Australia	11
5	Singapore	11
6	Spain	9
7	United Kingdom	9
8	Hong Kong	9
9	Italy	8
10	Germany	5

Keywords with the most papers, since 2023
#	Keyword	Papers
1	recommender systems	44
2	collaborative filtering	16
3	graph neural networks	11
4	cold start	10
5	data sparsity	10
6	large language models	10
7	personalized recommendation	8
8	federated learning	7
9	matrix factorization	7
10	sequential recommendation	7

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	405	4.7	Recommendation system based on deep learning methods: a systematic review and new directions	Artificial Intelligence Review	2019-08-03	Aminu Da’u, Naomie Binti Salim
2	314	3.9	Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data	Expert Systems with Applications	2020-01-25	N. Senthilselvan, V. Subramaniyaswamy, Sivaramakrishnan Natarajan et al.
3	229	2.2	Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy	IEEE Transactions on Knowledge and Data Engineering	2018-02-12	Hyejin Shin, Sungwook Kim, Junbum Shin et al.
4	196	2.5	A hybrid recommender system for recommending relevant movies using an expert system	Expert Systems with Applications	2020-05-13	Bogdan Walek, Vladimir Fojtik
5	178	1.6	Content-based filtering for recommendation systems using multiattribute networks	Expert Systems with Applications	2017-08-03	Jieun Son, Seoung Bum Kim

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Used together with#

Papers use MovieLens most often with Yelp (25), Location and social recommendation data (Gowalla, Foursquare, Yelp, Epinions) (22) and Amazon reviews (20); it has 23 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok. The whole network →

What papers use MovieLens together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
Yelp	Dataset	25	17.7
Location and social recommendation data (Gowalla, Foursquare, Yelp…	Dataset	22	27.4
Amazon reviews	Dataset	20	21.6
Netflix Prize	Dataset	15	107.8
MNIST / Fashion-MNIST	Dataset	13	3.0
Twitter / X	Dataset	13	0.8
Taobao / Tmall / JD.com	Dataset	12	10.7
YouTube / TikTok	Dataset	12	1.7
ImageNet	Dataset	10	1.1
IMDb	Dataset	9	9.9
Reddit	Dataset	8	3.4
Wikipedia	Dataset	8	2.0

Papers mentioning MovieLens in the last 30 days, newest first.
Date	Title	Journal	Authors	Affiliations
2026-10-17	IFCRec: interest-feature dual calibration for graph recommendation	Applied Intelligence	Zhenzhen Huang, Weiran Li, Shuo Xiao et al.	China University of Mining and Technology
2026-10-17	Language modeling on tabular data: a survey of foundations, techniques and evolution	Artificial Intelligence Review	Yucheng Ruan, Xiang Lan, Jingying Ma et al.	National University of Singapore; Institute of Data Science
2026-10-13	Posterior sampling of word embeddings	Statistics and Computing	Väinö Yrjänäinen, Isac Boström, Johan Jonasson et al.	Uppsala University; Chalmers University of Technology
2026-10-13	A hybrid framework of LLM-based interaction generation and adaptive sampler for recommendation enhancement	Information Processing & Management	Yijia Zhang, Wanyu Chen, Xiaosong Yuan et al.	National University of Defense Technology; Jilin University

Cite this page

Scholar4AI. (2026). MovieLens. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/movielens

@misc{scholar4ai-datasets-movielens,
  author = {{Scholar4AI}},
  title = {MovieLens},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/movielens}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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