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Click Through Rate Prediction On Avazu

Métriques

AUC

LogLoss

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AUC	LogLoss	Paper Title	Repository
OptEmbed	0.7902	0.374	OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction	-
Sparse Deep FwFM	0.7897	0.3748	DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving	-
DCNv3	0.7970	0.3695	FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction	-
Fi-GNN	0.7762	0.3825	Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction	-
FGCNN+IPNN	0.7883	0.3746	Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction	-
FinalMLP + MMBAttn	0.7666	-	MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction	-
FLEN	0.75	-	FLEN: Leveraging Field for Scalable CTR Prediction	-
CELS	0.8001	0.3678	Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
DNN + MMBAttn	0.765	-	MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction	-
OptInter	0.8062	0.3637	Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction	-
AutoInt	0.7752	0.3823	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	-
CETN	0.7962	-	CETN: Contrast-enhanced Through Network for CTR Prediction	-
AFN+	0.7555	-	Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions	-
OptInter-M	0.8060	0.3638	Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction	-
OptFS	0.795	0.3709	Optimizing Feature Set for Click-Through Rate Prediction	-

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