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 151–200 of 391 papers

TitleStatusHype
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data—0
JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer—0
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors—0
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction—0
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation—0
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions—0
LiRank: Industrial Large Scale Ranking Models at LinkedIn—0
Understanding and Counteracting Feature-Level Bias in Click-Through Rate PredictionCode0
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models—0
GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction—0
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation—0
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems—0
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation—0
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction—0
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction—0
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation—0
Temporal Importance Factor for Loss Functions for CTR Prediction—0
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate PredictionCode0
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction—0
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR PredictionCode0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions—0
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction—0
EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Prediction—0
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training—0
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR PredictionCode0
Making the Full Model Adaptive: Multi-level Domain Adaptation for Multi-Domain CTR Prediction—0
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation—0
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationCode0
AntM^2C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction—0
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction—0
STEC: See-Through Transformer-based Encoder for CTR Prediction—0
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction—0
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction—0
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
Deep Context Interest Network for Click-Through Rate Prediction—0
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services—0
TBIN: Modeling Long Textual Behavior Data for CTR Prediction—0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
Weighted Multi-Level Feature Factorization for App ads CTR and installation predictionCode0
Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models—0
News Popularity Beyond the Click-Through-Rate for Personalized Recommendations—0
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data—0
Confidence Ranking for CTR Prediction—0
ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer—0
A Collaborative Transfer Learning Framework for Cross-domain Recommendation—0
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction—0
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual RecommendationCode0
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao—0
Show:102550
← PrevPage 4 of 8Next →

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