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 101150 of 391 papers

TitleStatusHype
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate PredictionCode0
A Universal Framework for Compressing Embeddings in CTR PredictionCode0
Practice on Long Sequential User Behavior Modeling for Click-Through Rate PredictionCode0
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert SpecializationCode0
Product-based Neural Networks for User Response Prediction over Multi-field Categorical DataCode0
The Re-Label Method For Data-Centric Machine LearningCode0
ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature EmbeddingCode0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click PredictionCode0
Looking at CTR Prediction Again: Is Attention All You Need?Code0
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationCode0
AutoRec: An Automated Recommender SystemCode0
A Dual Input-aware Factorization Machine for CTR PredictionCode0
Click-Through Rate Prediction with the User Memory NetworkCode0
APG: Adaptive Parameter Generation Network for Click-Through Rate PredictionCode0
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
Joint Optimization of Ranking and Calibration with Contextualized Hybrid ModelCode0
Hybrid CNN Based Attention with Category Prior for User Image Behavior ModelingCode0
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate PredictionCode0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR PredictionCode0
Deep Interaction Machine: A Simple but Effective Model for High-order Feature InteractionsCode0
Deep Interest Evolution Network for Click-Through Rate PredictionCode0
GateNet: Gating-Enhanced Deep Network for Click-Through Rate PredictionCode0
Learning Feature Interactions with Lorentzian Factorization MachineCode0
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation SystemsCode0
CELA: Cost-Efficient Language Model Alignment for CTR PredictionCode0
FM^2: Field-matrixed Factorization Machines for Recommender SystemsCode0
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced RecommendationCode0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
FinalMLP: An Enhanced Two-Stream MLP Model for CTR PredictionCode0
Field-Embedded Factorization Machines for Click-through rate predictionCode0
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR PredictionCode0
Field-aware factorization machines for CTR predictionCode0
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display AdvertisingCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
Differentiable NAS Framework and Application to Ads CTR PredictionCode0
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR ChallengeCode0
Feature Generation by Convolutional Neural Network for Click-Through Rate PredictionCode0
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization MachineCode0
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce RecommendationCode0
Feature embedding in click-through rate predictionCode0
Feature Staleness Aware Incremental Learning for CTR PredictionCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
Deep Spatio-Temporal Neural Networks for Click-Through Rate PredictionCode0
Deep Session Interest Network for Click-Through Rate PredictionCode0
Deep Position-wise Interaction Network for CTR PredictionCode0
DKN: Deep Knowledge-Aware Network for News RecommendationCode0
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior DataCode0
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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
9STECAUC0.81Unverified
10DNN + MMBAttnAUC0.81Unverified
#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
5DeepFMAUC0.85Unverified
6DCNv2AUC0.85Unverified
#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
2FinalMLPAUC0.99Unverified
3FinalMLP + MMBAttnAUC0.99Unverified
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