Click Through Rate Prediction On Bing News
Métriques
AUC
Log Loss
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AUC | Log Loss |
|---|---|---|
| product-based-neural-networks-for-user | 0.8321 | 0.2775 |
| dkn-deep-knowledge-aware-network-for-news | 0.659 | - |
| xdeepfm-combining-explicit-and-implicit | 0.03 | 0.3382 |
| ripplenet-propagating-user-preferences-on-the | 0.678 | - |
| deepfm-a-factorization-machine-based-neural | 0.8376 | 0.2671 |
| wide-deep-learning-for-recommender-systems | 0.8377 | 0.2668 |
| xdeepfm-combining-explicit-and-implicit | 0.84 | 0.2649 |