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 151200 of 391 papers

TitleStatusHype
FinalMLP: An Enhanced Two-Stream MLP Model for CTR PredictionCode0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
Deep Interest Evolution Network for Click-Through Rate PredictionCode0
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce RecommendationCode0
Structured Semantic Model supported Deep Neural Network for Click-Through Rate PredictionCode0
Deep Spatio-Temporal Neural Networks for Click-Through Rate PredictionCode0
A Universal Framework for Compressing Embeddings in CTR PredictionCode0
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization MachineCode0
Feature embedding in click-through rate predictionCode0
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR ChallengeCode0
Practice on Long Sequential User Behavior Modeling for Click-Through Rate PredictionCode0
Feature Generation by Convolutional Neural Network for Click-Through Rate PredictionCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Deep Session Interest Network for Click-Through Rate PredictionCode0
Product-based Neural Networks for User Response PredictionCode0
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction0
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models0
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction0
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction0
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System0
Mutual Learning for Finetuning Click-Through Rate Prediction Models0
NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction0
News Popularity Beyond the Click-Through-Rate for Personalized Recommendations0
On-Device Model Fine-Tuning with Label Correction in Recommender Systems0
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction0
Online Interaction Detection for Click-Through Rate Prediction0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction0
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction0
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction0
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System0
PHN: Parallel heterogeneous network with soft gating for CTR prediction0
Polyhedral Conic Classifier for CTR Prediction0
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction0
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction0
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising0
Predicting clicks in online display advertising with latent features and side-information0
Provable Sparse Tensor Decomposition0
RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks0
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao0
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data0
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction0
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving0
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling0
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction0
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation0
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training0
Single-shot Embedding Dimension Search in Recommender System0
SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction0
Soft Retargeting Network for Click Through Rate Prediction0
Sparse Tensor Additive Regression0
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Benchmark Results

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