Click Through Rate Prediction On Ipinyou
Métriques
AUC
LogLoss
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AUC | LogLoss |
|---|---|---|
| memorize-factorize-or-be-naive-learning | 0.7825 | 0.005604 |
| product-based-neural-networks-for-user | 0.7661 | - |
| deep-learning-over-multi-field-categorical | 0.7619 | - |
| product-based-neural-networks-for-user | 0.7914 | - |
| product-based-neural-networks-for-user | 0.8174 | - |
| dcnv3-towards-next-generation-deep-cross | 0.7856 | 0.005535 |
| memorize-factorize-or-be-naive-learning | 0.7800 | 0.00564 |