ML
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Click-Through Rate Prediction | ML 1M | AUC0.9087 | 46 | |
| Dimension Selection | ML-1M | AUC80.99 | 31 | |
| Sequential Recommendation | ML-20M | NDCG@100.78 | 26 | |
| Recommendation | Ml 1M (test) | Recall21.2 | 24 | |
| Rubric satisfaction evaluation | ML | Claude-4 Sonnet Score36.7 | 24 | |
| Sequential Recommendation | ML-100K | NDCG@2013.29 | 22 | |
| Negative Constraint Recommendation | ML 1M | Recall@100.2076 | 22 | |
| Generative Recommendation | ML OOD 10M | Hit Rate @1061 | 18 | |
| Recommendation | ML-100K | HR@546.15 | 18 | |
| Collaborative Filtering | ML-20M large (test) | Recall@200.403 | 17 | |
| Sequential Recommendation | ML-10M | HR@575.91 | 15 | |
| Collaborative Filtering | ML-20M strong generalization | AOA Recall@200.3956 | 14 | |
| Multi-objective Re-ranking | ML-1M | HR@581.24 | 13 | |
| Collaborative Filtering | ML 10M | Recall@1019.34 | 12 | |
| Model Extraction | ML-1M (test) | N@1062.6 | 12 | |
| Recommendation | ML-100K | NDCG@119.21 | 11 | |
| CTR Prediction | ML-1M | AUC0.8194 | 11 | |
| Sequential Recommendation | ML 1M (test) | NDCG@1020.007 | 11 | |
| Sequential Recommendation | ML 32M | HR@518.02 | 10 | |
| Sequential Recommendation | ML-1M Head | NDCG@104.46 | 10 | |
| Recommendation | ML-10M (test) | RMSE0.777 | 10 | |
| Federated Recommendation and Attribute Unlearning | ML-100K Age attribute 1.0 (leave-one-out) | HR@1067.03 | 9 | |
| User behavior simulation | ML-100K | Precision71.99 | 9 | |
| Collaborative Filtering | ML-1M | HR@1031.79 | 9 | |
| Recommendation System Efficiency | ML 1M (overall) | Training Time (m)2.06 | 9 |
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