Click Through Rate Prediction On Ipinyou

Métriques

AUC

LogLoss

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
OPNN	0.8174	-	Product-based Neural Networks for User Response Prediction
IPNN	0.7914	-	Product-based Neural Networks for User Response Prediction
DCNv3	0.7856	0.005535	FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
OptInter	0.7825	0.005604	Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
OptInter-M	0.7800	0.00564	Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
PNN*	0.7661	-	Product-based Neural Networks for User Response Prediction
FNN	0.7619	-	Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction

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Click Through Rate Prediction On Ipinyou | SOTA | HyperAI