Collaborative Filtering On Movielens 10M
Métriques
RMSE
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | RMSE |
|---|---|
| on-the-difficulty-of-evaluating-baselines-a | 0.772 |
| dictionary-learning-for-massive-matrix | 0.799 |
| on-the-difficulty-of-evaluating-baselines-a | 0.7523 |
| on-the-difficulty-of-evaluating-baselines-a | 0.7563 |
| hybrid-recommender-system-based-on | 0.7954 |
| on-the-difficulty-of-evaluating-baselines-a | 0.823 |
| svd-ae-simple-autoencoders-for-collaborative | - |
| hybrid-recommender-system-based-on | 0.7767 |
| a-neural-autoregressive-approach-to | 0.771 |
| kernelized-synaptic-weight-matrices | 0.769 |
| exploring-the-multimodal-information-from | - |
| on-the-difficulty-of-evaluating-baselines-a | 0.7485 |
| a-federated-graph-neural-network-framework | 0.793 |
| fedgnn-federated-graph-neural-network-for | 0.803 |
| graph-convolutional-matrix-completion | 0.777 |
| mixture-rank-matrix-approximation-for | 0.7634 |
| autorec-autoencoders-meet-collaborative | 0.782 |