Click Through Rate Prediction On Kdd12
Métriques
AUC
Log Loss
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUC | Log Loss | Paper Title | Repository |
|---|---|---|---|---|
| OptFS | 0.7988 | 0.1527 | Optimizing Feature Set for Click-Through Rate Prediction | |
| OptEmbed | 0.8028 | 0.1521 | OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction | |
| AutoInt | 0.7881 | 0.1545 | AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | |
| DCNv3 | 0.8098 | 0.1494 | DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction | - |
| MemoNet | 0.8060 | - | MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction |
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