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 301350 of 391 papers

TitleStatusHype
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate PredictionCode1
Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate0
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior DataCode0
GateNet: Gating-Enhanced Deep Network for Click-Through Rate PredictionCode0
A Dual Input-aware Factorization Machine for CTR PredictionCode0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction0
TFNet: Multi-Semantic Feature Interaction for CTR Prediction0
AutoRec: An Automated Recommender SystemCode0
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate PredictionCode0
Category-Specific CNN for Visual-aware CTR Prediction at JD.com0
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate PredictionCode1
Training with Multi-Layer Embeddings for Model Reduction0
Differentiable Neural Input Search for Recommender Systems0
Feature Interaction based Neural Network for Click-Through Rate Prediction0
User Behavior Retrieval for Click-Through Rate PredictionCode1
Deep Interest with Hierarchical Attention Network for Click-Through Rate PredictionCode1
Deep Match to Rank Model for Personalized Click-Through Rate PredictionCode0
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate PredictionCode1
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction0
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training0
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized RecommendationsCode0
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad ServingCode1
Interpretable Click-Through Rate Prediction through Hierarchical AttentionCode1
Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature InteractionsCode0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions0
Learning Feature Interactions with Lorentzian Factorization MachineCode0
FLEN: Leveraging Field for Scalable CTR PredictionCode2
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction0
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction0
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR PredictionCode1
DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine0
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation SystemsCode0
Adaptive Factorization Network: Learning Adaptive-Order Feature InteractionsCode0
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced RecommendationCode0
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction0
Click-Through Rate Prediction with the User Memory NetworkCode0
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling0
Representation Learning-Assisted Click-Through Rate PredictionCode0
Deep Spatio-Temporal Neural Networks for Click-Through Rate PredictionCode0
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions0
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionCode1
Practice on Long Sequential User Behavior Modeling for Click-Through Rate PredictionCode0
Deep Session Interest Network for Click-Through Rate PredictionCode0
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization MachineCode0
Behavior Sequence Transformer for E-commerce Recommendation in AlibabaCode2
Learning Representations of Categorical Feature Combinations via Self-Attention0
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID EmbeddingsCode1
CPM-sensitive AUC for CTR prediction0
Block-distributed Gradient Boosted Trees0
Show:102550
← PrevPage 7 of 8Next →

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