Collaborative Filtering On Movielens 100K
المقاييس
Precision
RMSE (u1 Splits)
Recall
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | Precision | RMSE (u1 Splits) | Recall |
|---|---|---|---|
| ghrs-graph-based-hybrid-recommendation-system | 0.771 | 0.887 | 0.799 |
| deep-models-of-interactions-across-sets | - | 0.91 | - |
| inductive-graph-pattern-learning-for | - | 0.905 | - |
| matrix-completion-on-graphs | - | 0.996 | - |
| glocal-k-global-and-local-kernels-for | - | 0.8889 | - |
| geometric-matrix-completion-with-recurrent | - | 0.929 | - |
| fedgnn-federated-graph-neural-network-for | - | - | - |
| attribute-aware-non-linear-co-embeddings-of | - | 0.897 | - |
| graph-convolutional-matrix-completion | - | 0.905 | - |
| deep-models-of-interactions-across-sets | - | 0.920 | - |
| attribute-aware-non-linear-co-embeddings-of | - | 0.904 | - |
| collaborative-filtering-with-graph | - | 0.945 | - |
| a-federated-graph-neural-network-framework | - | - | - |
| scalable-probabilistic-matrix-factorization | - | 0.9174 | - |
| graph-convolutional-matrix-completion | - | 0.910 | - |
| interpretable-recommender-system-with | - | 0.890 | - |
| weighted-multi-level-feature-factorization | - | 0.928 | - |