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 101–150 of 391 papers

TitleStatusHype
Context-Aware Lifelong Sequential Modeling for Online Click-Through Rate Prediction—0
MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling—0
General Information Metrics for Improving AI Model Training Efficiency—0
Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction—0
Explainable CTR Prediction via LLM Reasoning—0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
LIBER: Lifelong User Behavior Modeling Based on Large Language Models—0
An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model—0
A Collaborative Ensemble Framework for CTR Prediction—0
Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale Click-Through Rate Prediction at Taobao—0
Towards Unifying Feature Interaction Models for Click-Through Rate Prediction—0
All-domain Moveline Evolution Network for Click-Through Rate Prediction—0
Collaborative Contrastive Network for Click-Through Rate Prediction—0
InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Graph Cross-Correlated Network for Recommendation—0
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction—0
A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining—0
Data Efficiency for Large Recommendation Models—0
NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction—0
RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks—0
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction—0
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding—0
CTR-KAN: KAN for Adaptive High-Order Feature Interaction ModelingCode0
AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising—0
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction—0
Moment&Cross: Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou—0
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR PredictionCode0
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction—0
TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou—0
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation—0
A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second—0
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions—0
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction—0
The Effects of Data Split Strategies on the Offline Experiments for CTR Prediction—0
Extreme Learning Machines for Fast Training of Click-Through Rate Prediction Models—0
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search—0
Star+: A New Multi-Domain Model for CTR Prediction—0
Mutual Learning for Finetuning Click-Through Rate Prediction Models—0
Polyhedral Conic Classifier for CTR Prediction—0
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising—0
Mitigate Position Bias with Coupled Ranking Bias on CTR Prediction—0
Look into the Future: Deep Contextualized Sequential Recommendation—0
CELA: Cost-Efficient Language Model Alignment for CTR PredictionCode0
A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features—0
Deep Pattern Network for Click-Through Rate 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