Click Through Rate Prediction On Kdd12

AUC

Log Loss

평가 결과

이 벤치마크에서 각 모델의 성능 결과

			Paper Title
DCNv3	0.8098	0.1494	FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
MemoNet	0.8060	-	MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
OptEmbed	0.8028	0.1521	OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
OptFS	0.7988	0.1527	Optimizing Feature Set for Click-Through Rate Prediction
AutoInt	0.7881	0.1545	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

0 of 5 row(s) selected.