---
title: "MovieLens: papers using this data, journals and trends"
description: "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)."
url: https://scholar4ai.com/datasets/movielens
markdown_url: https://scholar4ai.com/datasets/movielens.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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). (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# MovieLens

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/4.md)

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

- data category: Online reviews, e-commerce and recommender data
- 0 papers in the last 7 days
- Names searched for: MovieLens

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Online reviews, e-commerce and recommender data datasets: https://scholar4ai.com/datasets/online-reviews-e-commerce-and-recommender-data.md



## 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

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 8 | 6 | 7 | 5 | 9 | 5 | 16 | 32 | 25 | 33 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 1 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 2 | 2 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 4 | 2 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 3 |
| [Statistics](https://scholar4ai.com/categories/statistics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 2 |
| [Management](https://scholar4ai.com/categories/management.md) | 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](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 21 |
| 2 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 10 |
| 3 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 9 |
| 4 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 8 |
| 5 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 8 |
| 6 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 6 |
| 7 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 6 |
| 8 | [Mathematical Programming](https://scholar4ai.com/journals/mathematical-programming.md) | 5 |
| 9 | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 5 |
| 10 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 5 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 8 |
| 2 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 7 |
| 3 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 5 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 4 |
| 5 | [National University of Defense Technology](https://scholar4ai.com/institutions/national-university-of-defense-technology.md) | 4 |
| 6 | [The University of Sydney](https://scholar4ai.com/institutions/the-university-of-sydney.md) | 4 |
| 7 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 4 |
| 8 | [Alibaba Group (China)](https://scholar4ai.com/companies/alibaba-group-china.md) | 3 |
| 9 | [Beihang University](https://scholar4ai.com/institutions/beihang-university.md) | 3 |
| 10 | [Carnegie Mellon University](https://scholar4ai.com/institutions/carnegie-mellon-university.md) | 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](https://scholar4ai.com/keywords/recommender-systems.md) | 44 |
| 2 | collaborative filtering | 16 |
| 3 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 11 |
| 4 | cold start | 10 |
| 5 | data sparsity | 10 |
| 6 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 10 |
| 7 | personalized recommendation | 8 |
| 8 | [federated learning](https://scholar4ai.com/keywords/federated-learning.md) | 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](https://doi.org/10.1007/s10462-019-09744-1) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 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](https://doi.org/10.1016/j.eswa.2020.113248) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 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](https://doi.org/10.1109/tkde.2018.2805356) | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 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](https://doi.org/10.1016/j.eswa.2020.113452) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2020-05-13 | Bogdan Walek, Vladimir Fojtik |
| 5 | 178 | 1.6 | [Content-based filtering for recommendation systems using multiattribute networks](https://doi.org/10.1016/j.eswa.2017.08.008) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2017-08-03 | Jieun Son, Seoung Bum Kim |

## 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.

*What papers use MovieLens together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Yelp](https://scholar4ai.com/datasets/yelp.md) | Dataset | 25 | 17.7 |
| [Location and social recommendation data (Gowalla, Foursquare, Yelp…](https://scholar4ai.com/datasets/location-and-social-recommendation-data-gowalla-foursquare-yelp-epinions.md) | Dataset | 22 | 27.4 |
| [Amazon reviews](https://scholar4ai.com/datasets/amazon-reviews.md) | Dataset | 20 | 21.6 |
| [Netflix Prize](https://scholar4ai.com/datasets/netflix-prize.md) | Dataset | 15 | 107.8 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 13 | 3.0 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 13 | 0.8 |
| [Taobao / Tmall / JD.com](https://scholar4ai.com/datasets/taobao-tmall-jd-com.md) | Dataset | 12 | 10.7 |
| [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | Dataset | 12 | 1.7 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 10 | 1.1 |
| [IMDb](https://scholar4ai.com/datasets/imdb.md) | Dataset | 9 | 9.9 |
| [Reddit](https://scholar4ai.com/datasets/reddit.md) | Dataset | 8 | 3.4 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 8 | 2.0 |

The whole network: https://scholar4ai.com/code/network.md

## Papers

*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](https://doi.org/10.1007/s10489-026-07531-4) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 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](https://doi.org/10.1007/s10462-026-11726-z) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Yucheng Ruan, Xiang Lan, Jingying Ma et al. | National University of Singapore; Institute of Data Science |
| 2026-10-13 | [Posterior sampling of word embeddings](https://doi.org/10.1007/s11222-026-10996-5) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 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](https://doi.org/10.1016/j.ipm.2026.105206) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 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

```bibtex
@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}
}
```

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