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SOTA
Klickratevorhersage
Click Through Rate Prediction On Avazu
Click Through Rate Prediction On Avazu
Metriken
AUC
LogLoss
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
AUC
LogLoss
Paper Title
OptInter
0.8062
0.3637
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
OptInter-M
0.8060
0.3638
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
CELS
0.8001
0.3678
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
DCNv3
0.7970
0.3695
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
CETN
0.7962
-
CETN: Contrast-enhanced Through Network for CTR Prediction
OptFS
0.795
0.3709
Optimizing Feature Set for Click-Through Rate Prediction
OptEmbed
0.7902
0.374
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Sparse Deep FwFM
0.7897
0.3748
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
FGCNN+IPNN
0.7883
0.3746
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Fi-GNN
0.7762
0.3825
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
AutoInt
0.7752
0.3823
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
FinalMLP + MMBAttn
0.7666
-
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction
DNN + MMBAttn
0.765
-
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction
AFN+
0.7555
-
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
FLEN
0.75
-
FLEN: Leveraging Field for Scalable CTR Prediction
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Click Through Rate Prediction On Avazu | SOTA | HyperAI