Click Through Rate Prediction On Dianping
Métriques
AUC
Log Loss
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUC | Log Loss | Paper Title | Repository |
|---|---|---|---|---|
| Wide & Deep | 0.8361 | 0.3364 | Wide & Deep Learning for Recommender Systems | - |
| DeepFM | 0.8481 | 0.3333 | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | - |
| PNN | 0.8445 | 0.3424 | Product-based Neural Networks for User Response Prediction | - |
| DNN | 0.8318 | - | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | - |
| xDeepFM | 0.8639 | 0.3156 | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | - |
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