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Click Through Rate Prediction On Avazu

Metriken

AUC

LogLoss

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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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Click Through Rate Prediction On Avazu | SOTA | HyperAI