MovieLens
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Recommendation | MovieLens-1M (test) | NDCG@2019.7 | 116 | |
| Recommendation | MovieLens | Accuracy97.9 | 99 | |
| CTR Prediction | MovieLens | AUC97.52 | 87 | |
| Recommendation Diversity | MovieLens | Mean Diversity40.12 | 80 | |
| Novel Recommendation | MovieLens | Min Score196.54 | 70 | |
| Recommendation | MovieLens 1M | nDCG@1056.197 | 62 | |
| Recommendation | MovieLens 100K (test) | RMSE0.883 | 60 | |
| Group Attack | MovieLens Unpopular items 1M | D@10-0.0009 | 52 | |
| Group Attack | MovieLens Popular items 1M | D@100.1103 | 52 | |
| Vehicle Edge Caching | MovieLens 1M (test) | Cache Hit Rate53.05 | 48 | |
| Recommendation | Movielens | Recall@2028.65 | 44 | |
| Sequential Recommendation | MovieLens-1M (test) | Hit@1082.45 | 42 | |
| Sequential Recommendation | MovieLens | ValidRatio1 | 41 | |
| Matrix Estimation | MovieLens Symmetric Noise | L1 Distance Error0.7042 | 40 | |
| Matrix Estimation | MovieLens Pairflip Noise | L1 Distance Error0.9449 | 40 | |
| Matrix Completion | MovieLens-1M (test) | RMSE0.822 | 40 | |
| 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 | Recall@108.65 | 32 | |
| 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 | |
| Recommendation | Movielens (warm) | Recall@2029.87 | 30 | |
| Recommendation | Movielens (cold) | Recall@2023.41 | 30 |
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