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MovieLens dataset and benchmarks – Page 10 · SOTA2 Research

Research Datasets

MovieLens

Benchmarks

Task NameDataset NameSOTA MethodSort

Task NameDataset NameSOTA ResultTrendResults

Click-Through Rate Prediction

MovieLens Federated Non-IID 10 clients 1M

77.2AUC

3

Click-Through Rate Prediction

MovieLens Federated IID 10 clients 1M

79.6AUC

3

Click-Through Rate Prediction

MovieLens Centralized 1M (train val test 80-10-10)

84.8AUC

3

Decision Making

MovieLens (held-out)

100Accuracy

3

MovieLens-1M Tiny Scale (temporal holdout)

0.95Prep. Time (s)

3

Latent Factor Analysis

D2 MovieLens 1M (train/val)

0.855RMSE

3

MovieLens 799 users sampled (test)

72Recall

3

Membership Inference Attack

MovieLens ML-1M and ML-100K avg across 4 settings

10.4Time Cost (avg)

3

Matrix Completion

MovieLens 20M

0.789RMSE (10-NN)

3

Robust Matrix Factorization

MovieLens 10M (test)

0.715MABS

2

Robust Matrix Factorization

MovieLens 1M (test)

0.728MABS

2

Distributional Prediction

MovieLens 80/20 split (test)

0.188Total Variation (TV)

2

Adversarial Attack on Neural Contextual Bandits

MovieLens

226Regret

2

MovieLens 32M Small Scale (temporal holdout)

7.91Preparation Time

2

MovieLens

23.49Precision@20

2

Personalized recommendation

Movielens1M

1Inference Latency (ms)

2

Unconstrained DPP MAP inference

MovieLens

37,829.5Product MAP

2

Density Estimation

MovieLens 20M

0.986Pearson Correlation

2

Pair-wise edge prediction

MovieLens non-k-uniform (test)

99.9AUC

2

Collaborative Filtering

MovieLens random 25% 10M (test)

0.799Test RMSE

2

Experimental Design Selection

MovieLens graph-recommendation substrate appendix

1.976Risk Score

1

Session-based next-item recommendation

MovieLens-20M (test)

4.82Hit Rate@1

1

Session-based next-item recommendation

MovieLens 100K (test)

1.8Hit Rate@1

1

Subjective reliability estimation

MovieLens

—Primary metric

0

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