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MovieLens

Introduced in The MovieLens Datasets: History and Context1 Jan 2016 archive 2025-07-28

The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations.

Source: The MovieLens Datasets: History and Context Image Source: http://files.grouplens.org/papers/harper-tiis2015.pdf

HomepageBenchmarksPapers JSON

Benchmarks archive 2025-07-28

All 17 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Recommendation Systems MovieLens 1M GLocal-K RMSE 0.8227 GLocal-K: Global and Local Kernels for Recommender Systems usydnlp/Glocal_K +2 31 Compare
Recommendation Systems MovieLens 100K CDLD-GR (doi: 10.5281/zenodo.15851754) RMSE (u1 Splits) 0.884 — — 18 Compare
Recommendation Systems MovieLens 20M HyperML nDCG@10 0.6404 HyperML: A Boosting Metric Learning Approach in... — 18 Compare
Recommendation Systems MovieLens 10M Bayesian timeSVD++ flipped RMSE 0.7485 On the Difficulty of Evaluating Baselines: A Study on... srendle/libfm +1 17 Compare
Link Prediction MovieLens 25M PEAGAT nDCG@10 0.5475 Metapath- and Entity-aware Graph Neural Network for... ecml-peagnn/PEAGNN 7 Compare
Click-Through Rate Prediction MovieLens 20M github.com/guotong1988/movielens_dataset AUC 0.79 — — 6 Compare
Click-Through Rate Prediction MovieLens 1M STEC AUC 0.9712 STEC: See-Through Transformer-based Encoder for CTR Prediction — 6 Compare
Movie Recommendation MovieLens 1M BPR NDCG 0.33 Post Processing Recommender Systems with Knowledge... giacoballoccu/explanation-quality-recsys 5 Compare
Collaborative Filtering MovieLens 1M SimpleX NDCG@20 0.2670 SimpleX: A Simple and Strong Baseline for Collaborative Filtering reczoo/RecZoo 4 Compare
Multibehavior Recommendation MovieLens HMAR HR@10 0.9412 HMAR: Hierarchical Masked Attention for Multi-Behaviour... shereen-elsayed/hmar 4 Compare
Sequential Recommendation MovieLens 1M TiM4Rec HR@5 0.2308 TiM4Rec: An Efficient Sequential Recommendation Model... alwaysfhao/tim4rec 4 Compare
Click-Through Rate Prediction MovieLens TF4CTR AUC 0.9746 TF4CTR: Twin Focus Framework for CTR Prediction via... salmon1802/tf4ctr 3 Compare
Link Prediction MovieLens 1M Hyper-SAGNN-W AUPR 0.81 Hyper-SAGNN: a self-attention based graph neural network... ma-compbio/Hyper-SAGNN 2 Compare
Knowledge Graph Completion MovieLens 1M KTUP (soft) Hits@10 48.9 Unifying Knowledge Graph Learning and Recommendation:... TaoMiner/joint-kg-recommender 1 Compare
Multi-Media Recommendation MovieLens LightGT Recall@10 0.2650 LightGT: A Light Graph Transformer for Multimedia Recommendation Liuwq-bit/LightGT 1 Compare
Multi-Media Recommendation MovieLens 10M MMGCN NDCG 0.3062 MMGCN: Multi-modal Graph Convolution Network for... weiyinwei/mmgcn 1 Compare
Recommendation Systems MovieLens-Latest RATE-CSE Recall@10 0.3225 Collaborative Similarity Embedding for Recommender Systems cnclabs/smore +1 1 Compare

Papers archive 2025-07-28

30 shown of 84 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 1,246. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
SS4Rec: Continuous-Time Sequential Recommendation with State Space Models 1 1 12 Feb 2025 not harvested
Multi-Behavioral Sequential Recommendation 1 1 8 Oct 2024 not harvested
TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model 1 1 24 Sep 2024 not harvested
Retrieval with Learned Similarities 1 1 22 Jul 2024 not harvested
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction 2 1 18 Jul 2024 not harvested
SVD-AE: Simple Autoencoders for Collaborative Filtering 2 2 8 May 2024 ran 3 of 5 samples (2 unverified)
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation 1 1 6 May 2024 not harvested
HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation 1 3 29 Apr 2024 not harvested
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations 12 2 27 Feb 2024 ran 14 of 42 samples (28 unverified; 3 pointer-only for licence)
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention 2 1 16 Dec 2023 ran 7 of 7 samples (0 unverified; 7 pointer-only for licence)
STEC: See-Through Transformer-based Encoder for CTR Prediction 0 1 29 Aug 2023 not harvested
Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction 1 1 3 Aug 2023 not harvested
LightGT: A Light Graph Transformer for Multimedia Recommendation 1 1 18 Jul 2023 not harvested
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction 4 1 3 Apr 2023 ran 13 of 16 samples (3 unverified)
Infinite Recommendation Networks: A Data-Centric Approach 5 1 3 Jun 2022 ran 6 of 13 samples (7 unverified)
A federated graph neural network framework for privacy-preserving personalization 1 3 2 Jun 2022 not harvested
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations 1 4 24 Apr 2022 not harvested
Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud 1 1 30 Dec 2021 not harvested
GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation 1 3 6 Nov 2021 not harvested
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation 2 1 28 Oct 2021 not harvested
SimpleX: A Simple and Strong Baseline for Collaborative Filtering 1 1 26 Sep 2021 not harvested
GLocal-K: Global and Local Kernels for Recommender Systems 3 2 27 Aug 2021 not harvested
Inductive Matrix Completion Using Graph Autoencoder 2 1 25 Aug 2021 not harvested
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP) 1 1 10 Feb 2021 not harvested
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation 0 3 9 Feb 2021 not harvested
Lightweight representation learning for efficient and scalable recommendation 1 1 4 Jan 2021 not harvested
The complementarity of a diverse range of deep learning features extracted from video content for video recommendation 1 1 21 Nov 2020 not harvested
Metapath- and Entity-aware Graph Neural Network for Recommendation 1 1 22 Oct 2020 not harvested
Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective 1 1 20 Sep 2020 not harvested
Disentangled Graph Collaborative Filtering 2 1 3 Jul 2020 ran 5 of 6 samples (1 unverified; 6 pointer-only for licence)

The full list of 84 is in the JSON twin.

Dataset loaders archive 2025-07-28

www.tensorflow.org/datasets/catalog/movielenstensorflow/datasetstfjax

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Link PredictionRecommendation SystemsKnowledge Graph CompletionClick-Through Rate PredictionSequential RecommendationCollaborative FilteringMulti-Media RecommendationMultibehavior RecommendationExplainable RecommendationRecommendation Systems (Item cold-start)Movie Recommendation

License archive 2025-07-28

Custom

Modalities archive 2025-07-28

Tabular

Languages archive 2025-07-28

EnglishSpanish

Variants archive 2025-07-28

  • MovieLens
  • MovieLens 100K
  • MovieLens 1M
  • MovieLens 10M
  • MovieLens 20M
  • MovieLens-Latest
  • MovieLens 25M

7 variant names, as the archive lists them.

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