Click Through Rate Prediction On Criteo

평가 지표

AUC

Log Loss

평가 결과

이 벤치마크에서 각 모델의 성능 결과

			Paper Title
DCNv3	0.8162	0.4358	FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
GDCN	0.8161	0.4360	Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
MemoNet	0.8152	-	MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
TF4CTR	0.8150	-	TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
FinalMLP + MMBAttn	0.81497	-	MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction
FinalMLP	0.8149	-	FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
CETN	0.8148	0.4373	CETN: Contrast-enhanced Through Network for CTR Prediction
DNN + MMBAttn	0.8143	-	MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction
STEC	0.8143	0.4379	STEC: See-Through Transformer-based Encoder for CTR Prediction
MaskNet	0.8131	-	MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
DeepLight	0.8123	0.4395	DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
CELS	0.8117	0.4400	Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
OptFS	0.8116	0.4401	Optimizing Feature Set for Click-Through Rate Prediction
DCN V2	0.8115	0.4406	DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
OptEmbed	0.8114	0.44	OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
ContextNet	0.8113	-	ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
FiBiNet++	0.8110	-	FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
NormDNN	0.8107	-	Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction
DeepFFM	0.8104	0.4416	FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
FiBiNET	0.8103	0.4423	-

0 of 38 row(s) selected.

Command Palette

Click Through Rate Prediction On Criteo

평가 지표

평가 결과