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 51100 of 391 papers

TitleStatusHype
FINT: Field-aware INTeraction Neural Network For CTR PredictionCode1
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information NetworkCode1
Triangle Graph Interest Network for Click-through Rate PredictionCode1
DeepFM: A Factorization-Machine based Neural Network for CTR PredictionCode1
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate PredictionCode1
Knowledge Graph Convolutional Networks for Recommender SystemsCode1
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPUCode1
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate PredictionCode1
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender SystemsCode1
Interpretable Click-Through Rate Prediction through Hierarchical AttentionCode1
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate PredictionCode1
Deep Interest Network for Click-Through Rate PredictionCode1
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionCode1
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature RankingCode1
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank SystemsCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Deep & Cross Network for Ad Click PredictionsCode1
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced RecommendationCode1
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate PredictionCode1
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR PredictionCode1
Deep Interest with Hierarchical Attention Network for Click-Through Rate PredictionCode1
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at EtsyCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait IssueCode1
Discrete Semantic Tokenization for Deep CTR PredictionCode1
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge DistillationCode1
Enhancing CTR Prediction with Context-Aware Feature Representation LearningCode1
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID EmbeddingsCode1
Hybrid CNN Based Attention with Category Prior for User Image Behavior ModelingCode0
CTR-KAN: KAN for Adaptive High-Order Feature Interaction ModelingCode0
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
AutoRec: An Automated Recommender SystemCode0
GateNet: Gating-Enhanced Deep Network for Click-Through Rate PredictionCode0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
Joint Optimization of Ranking and Calibration with Contextualized Hybrid ModelCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
FM^2: Field-matrixed Factorization Machines for Recommender SystemsCode0
Adaptive Factorization Network: Learning Adaptive-Order Feature InteractionsCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual RecommendationCode0
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
Field-Embedded Factorization Machines for Click-through rate predictionCode0
Field-aware factorization machines for CTR predictionCode0
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display AdvertisingCode0
Show:102550
← PrevPage 2 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