Click Through Rate Prediction On Criteo

评估指标

AUC

Log Loss

评测结果

各个模型在此基准测试上的表现结果

模型名称	AUC	Log Loss	Paper Title	Repository
IPNN	0.7972	0.45323	Product-based Neural Networks for User Response 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
FiBiNet++	0.8110	-	FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
GDCN	0.8161	0.4360	Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
xDeepFM	0.8052	0.4418	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
TFNet	0.7991	-	TFNet: Multi-Semantic Feature Interaction for CTR Prediction	-
CETN	0.8148	0.4373	CETN: Contrast-enhanced Through Network for CTR Prediction
FNN	0.7963	0.45738	Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
FiBiNET	0.8103	0.4423	FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
MemoNet	0.8152	-	MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction
AutoDeepFM(3rd)	0.8010	0.5405	AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Wide&Deep	0.7981	0.46772	Wide & Deep Learning for Recommender Systems
Fi-GNN	0.8062	0.4453	Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
DeepFM	0.8007	0.45083	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
DeepLight	0.8123	0.4395	DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
OptInter	0.8101	0.4417	Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
XCrossNet	0.8067	-	XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
TF4CTR	0.8150	-	TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
NormDNN	0.8107	-	Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction

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