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HyperAI超神経
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プラットフォーム
ホーム
SOTA
クリックスルー率予測
Click Through Rate Prediction On Criteo
Click Through Rate Prediction On Criteo
評価指標
AUC
Log Loss
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
AUC
Log Loss
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.
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