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 251–300 of 391 papers

TitleStatusHype
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction—0
Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction—0
A Deep Behavior Path Matching Network for Click-Through Rate Prediction—0
AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction—0
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction—0
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction—0
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction—0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
A General Framework for Debiasing in CTR Prediction—0
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction—0
AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising—0
All-domain Moveline Evolution Network for Click-Through Rate Prediction—0
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model—0
An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify—0
An Incremental Learning framework for Large-scale CTR Prediction—0
A Non-sequential Approach to Deep User Interest Model for CTR Prediction—0
AntM^2C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction—0
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction—0
A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining—0
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation—0
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling—0
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction—0
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction—0
Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction—0
Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders—0
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
Block-distributed Gradient Boosted Trees—0
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale Click-Through Rate Prediction at Taobao—0
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors—0
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction—0
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models—0
CAN: Feature Co-Action for Click-Through Rate Prediction—0
CareGraph: A Graph-based Recommender System for Diabetes Self-Care—0
Category-Specific CNN for Visual-aware CTR Prediction at JD.com—0
Causality-based CTR Prediction using Graph Neural Networks—0
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction—0
Click Through Rate Prediction for Contextual Advertisment Using Linear Regression—0
Click-Through Rate Prediction in Online Advertising: A Literature Review—0
Click-Through Rate Prediction Using Graph Neural Networks and Online Learning—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction—0
Collaborative Contrastive Network for Click-Through Rate Prediction—0
Communication-Efficient TeraByte-Scale Model Training Framework for Online Advertising—0
On the Adaptation to Concept Drift for CTR Prediction—0
Confidence Ranking for CTR Prediction—0
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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