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Papers Benchmarks Datasets Tasks

MovieLens dataset and benchmarks – Page 4 · SOTA2 Research

Research Datasets

MovieLens

Benchmarks

Task NameDataset NameSOTA MethodSort

Task NameDataset NameSOTA ResultTrendResults

Collaborative Filtering

MovieLens 10M (test)

0.771RMSE

15

Denoising Recommendation

MovieLens 1M

20.15Recall@10

14

Top-K Recommendation

MovieLens (test)

42.52R@50

14

Sequential Recommendation

Movielens 1M

77.32AUC

14

MovieLens 1M

23.96Recall@20

14

MovieLens 100K

35.66Recall@20

14

MovieLens 1M

0.54Latency (s)

14

MovieLens 20M (30% temporal test)

6.2LtC

14

MovieLens (Phase-III)

80.69AUC

14

MovieLens (Phase-II)

80.38AUC

14

MovieLens (Phase-I)

79.96AUC

14

Likelihood Estimation

MovieLens 10k (test)

-0.349Context Likelihood

14

MovieLens 1M

0.846AUC

14

Rating Prediction

MovieLens 100K (80/20 train test)

0.905RMSE

14

Sparse Predictive Analytics

MovieLens (test)

0.444RMSE

14

Conversational Recommendation

MovieLens 1M (sampled)

53.2S@3

13

MovieLens 20M

84.5AUC

13

MovieLens (test)

26.31Hit@1

13

User Simulation

MovieLens

29.74F1 Score

13

Occupation Inference

MovieLens 1m

15.62Accuracy (β=0.1)

13

MovieLens 1m

38.83Acc (β=0.1)

13

Multi-scenario Multi-task Recommendation

MovieLens-1M

0.819AUC (S1, T1)

13

Rating Prediction

MovieLens

0.813MSE

12

Sequential Recommendation

MovieLens 20M

22.76R@10

12

Ranking Uncertainty Quantification

MovieLens 1M

0.158Kendall's Tau@5

12

Page 4 of 10

Popular tasks

Recommendation CTR Prediction Collaborative Filtering Sequential Recommendation Matrix completion

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