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
Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction—0
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce RecommendationCode0
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search—0
FinalMLP: An Enhanced Two-Stream MLP Model for CTR PredictionCode0
The Re-Label Method For Data-Centric Machine LearningCode0
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at KuaishouCode1
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives—0
A Deep Behavior Path Matching Network for Click-Through Rate Prediction—0
Causality-based CTR Prediction using Graph Neural Networks—0
Decision-Making Context Interaction Network for Click-Through Rate Prediction—0
Optimizing Feature Set for Click-Through Rate PredictionCode1
AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction—0
DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction—0
xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactionsCode0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction—0
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge DistillationCode1
Deep Intention-Aware Network for Click-Through Rate Prediction—0
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR PredictionCode0
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling—0
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR PredictionCode1
On-Device Model Fine-Tuning with Label Correction in Recommender Systems—0
Deep Multi-Representation Model for Click-Through Rate PredictionCode0
Clustering the Sketch: A Novel Approach to Embedding Table CompressionCode1
SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction—0
KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction—0
Recurrent Meta-Learning against Generalized Cold-start Problem in CTR PredictionCode0
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News RecommendationCode0
Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction—0
Boost CTR Prediction for New Advertisements via Modeling Visual Content—0
Feature embedding in click-through rate predictionCode0
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services—0
FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR PredictionCode1
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction ModelsCode1
An Incremental Learning framework for Large-scale CTR Prediction—0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction—0
Causal Inference in Recommender Systems: A Survey and Future DirectionsCode1
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction—0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model—0
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao—0
OptEmbed: Learning Optimal Embedding Table for Click-through Rate PredictionCode1
Sparse Attentive Memory Network for Click-through Rate Prediction with Long SequencesCode0
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding—0
NASRec: Weight Sharing Neural Architecture Search for Recommender SystemsCode1
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction—0
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction—0
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving—0
0/1 Deep Neural Networks via Block Coordinate Descent—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