MovieLens
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Recommendation | MovieLens | Accuracy97.9 | 84 | |
| Recommendation Diversity | MovieLens | Mean Diversity40.12 | 80 | |
| Novel Recommendation | MovieLens | Min Score196.54 | 70 | |
| Recommendation | MovieLens 100K (test) | RMSE0.883 | 55 | |
| CTR Prediction | MovieLens | AUC97.15 | 55 | |
| Vehicle Edge Caching | MovieLens 1M (test) | Cache Hit Rate53.05 | 48 | |
| Recommendation | MovieLens-1M (test) | NDCG@567.84 | 46 | |
| Sequential Recommendation | MovieLens-1M (test) | Hit@1082.45 | 42 | |
| Sequential Recommendation | MovieLens | ValidRatio1 | 41 | |
| Multi-objective Recommendation | MovieLens Individual User Instances | SM19.1856 | 35 | |
| Multi-objective Recommendation | MovieLens | DM Score24.72 | 35 | |
| Multi-objective Recommendation | MovieLens | CLO0.9541 | 35 | |
| Recommendation | MovieLens 10M (test) | Recall@109.35 | 32 | |
| Recommendation | MovieLens 10M (Set-up (S)) | Recall@1027.68 | 32 | |
| Personalized Prediction | MovieLens (test) | Accuracy0.646 | 32 | |
| Matrix Completion | MovieLens-1M (test) | RMSE0.822 | 30 | |
| Recommendation | MovieLens small-scale | LCS Score66.8315 | 30 | |
| Recommendation | MovieLens 20M | nDCG@1064.042 | 29 | |
| Rating Prediction | MovieLens 90/10 1M (train test) | RMSE0.829 | 27 | |
| Gender Attribute Inference | MovieLens 1m | F1 (beta=0.1)76.14 | 26 | |
| Collaborative Filtering | MovieLens 1M (test) | RMSE0.829 | 25 | |
| Recommendation | MovieLens 20M (test) | Accuracy67.4 | 24 | |
| top-n recommendation | MovieLens 20M | NDCG@1000.448 | 22 | |
| CTR prediction | MovieLens (test) | Logloss0.1857 | 21 | |
| Matrix Completion | MovieLens-100K (test) | RMSE0.897 | 21 |
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