SOTAVerified

Click-Through Rate Prediction

Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )

Papers

Showing 26–50 of 391 papers

TitleStatusHype
Towards Deeper, Lighter and Interpretable Cross Network for CTR PredictionCode1
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature RankingCode1
Temporal Interest Network for User Response PredictionCode1
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate PredictionCode1
Reformulating CTR Prediction: Learning Invariant Feature Interactions for RecommendationCode1
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR PredictionCode1
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at KuaishouCode1
Optimizing Feature Set for Click-Through Rate PredictionCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge DistillationCode1
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR PredictionCode1
Clustering the Sketch: A Novel Approach to Embedding Table CompressionCode1
FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR PredictionCode1
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction ModelsCode1
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
OptEmbed: Learning Optimal Embedding Table for Click-through Rate PredictionCode1
NASRec: Weight Sharing Neural Architecture Search for Recommender SystemsCode1
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate PredictionCode1
Enhancing CTR Prediction with Context-Aware Feature Representation LearningCode1
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPUCode1
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce SearchCode1
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate PredictionCode1
Real World Large Scale Recommendation Systems Reproducibility and Smooth ActivationsCode1
Triangle Graph Interest Network for Click-through Rate PredictionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1QNN-αAUC0.82—Unverified
2FCNAUC0.82—Unverified
3GDCNAUC0.82—Unverified
4MemoNetAUC0.82—Unverified
5TF4CTRAUC0.82—Unverified
6FinalMLP + MMBAttnAUC0.81—Unverified
7FinalMLPAUC0.81—Unverified
8CETNAUC0.81—Unverified
9DNN + MMBAttnAUC0.81—Unverified
10STECAUC0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1OptInterAUC0.81—Unverified
2OptInter-MAUC0.81—Unverified
3CELSAUC0.8—Unverified
4FCNAUC0.8—Unverified
5CETNAUC0.8—Unverified
6OptFSAUC0.8—Unverified
7OptEmbedAUC0.79—Unverified
8Sparse Deep FwFMAUC0.79—Unverified
9FGCNN+IPNNAUC0.79—Unverified
10Fi-GNNAUC0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepFMAUC0.87—Unverified
2FNNAUC0.87—Unverified
3Wide & Deep (LR & DNN)AUC0.87—Unverified
4PNN*AUC0.87—Unverified
5IPNNAUC0.87—Unverified
6Wide & Deep (FM & DNN)AUC0.87—Unverified
7OPNNAUC0.87—Unverified
8DeepMCPAUC0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1xDeepFMAUC0.84—Unverified
2Wide & DeepAUC0.84—Unverified
3DeepFMAUC0.84—Unverified
4PNNAUC0.83—Unverified
5RippleNetAUC0.68—Unverified
6DKNAUC0.66—Unverified
7DNNAUC0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1OPNNAUC0.82—Unverified
2IPNNAUC0.79—Unverified
3FCNAUC0.79—Unverified
4OptInterAUC0.78—Unverified
5OptInter-MAUC0.78—Unverified
6PNN*AUC0.77—Unverified
7FNNAUC0.76—Unverified
#ModelMetricClaimedVerifiedStatus
1FCNAUC0.86—Unverified
2DeepIMAUC0.85—Unverified
3xDeepFMAUC0.85—Unverified
4AutoInt+AUC0.85—Unverified
5DCNv2AUC0.85—Unverified
6DeepFMAUC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1STECAUC0.97—Unverified
2KNIAUC0.94—Unverified
3RippleNetAUC0.92—Unverified
4MKRAUC0.92—Unverified
5DCNv3AUC0.91—Unverified
6AutoIntAUC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1github.com/guotong1988/movielens_datasetAUC0.79—Unverified
2DIN + Dice ActivationAUC0.73—Unverified
3DINAUC0.73—Unverified
4DeepFMAUC0.73—Unverified
5PNNAUC0.73—Unverified
6Wide & DeepAUC0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1DIN + Dice ActivationAUC0.89—Unverified
2DINAUC0.88—Unverified
3DeepFMAUC0.87—Unverified
4PNNAUC0.87—Unverified
5Wide & DeepAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1xDeepFMAUC0.86—Unverified
2DeepFMAUC0.85—Unverified
3PNNAUC0.84—Unverified
4Wide & DeepAUC0.84—Unverified
5DNNAUC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1TF4CTRAUC0.99—Unverified
2FinalMLP + MMBAttnAUC0.99—Unverified
3FinalMLPAUC0.99—Unverified
4DNN + MMBAttnAUC0.99—Unverified
5AFN+AUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1FCNAUC0.81—Unverified
2MemoNetAUC0.81—Unverified
3OptEmbedAUC0.8—Unverified
4OptFSAUC0.8—Unverified
5AutoIntAUC0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1TF4CTRAUC0.97—Unverified
2FinalMLPAUC0.97—Unverified
3AFN+AUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1DSTN-IAUC0.84—Unverified
2DeepMCPAUC0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1KGCN-sumAUC0.74—Unverified
2RippleNetAUC0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1KGCN-concatAUC0.8—Unverified
2MKRAUC0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1DIENAUC0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1NormDNNAUC0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1MKRAUC0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1FGCNN+IPNNAUC0.94—Unverified