Click Through Rate Prediction On Company
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 (FM & DNN) | 0.8661 | 0.02640 | Wide & Deep Learning for Recommender Systems | - |
| DeepMCP | 0.7674 | 0.2341 | Representation Learning-Assisted Click-Through Rate Prediction | - |
| OPNN | 0.8658 | 0.02641 | Product-based Neural Networks for User Response Prediction | - |
| Wide & Deep (LR & DNN) | 0.8673 | 0.02634 | Wide & Deep Learning for Recommender Systems | - |
| DeepFM | 0.8715 | 0.02618 | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | - |
| PNN* | 0.8672 | 0.02636 | Product-based Neural Networks for User Response Prediction | - |
| FNN | 0.8683 | 0.02629 | Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction | - |
| IPNN | 0.8664 | 0.02637 | Product-based Neural Networks for User Response Prediction | - |
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