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 51–100 of 391 papers

TitleStatusHype
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced RecommendationCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
AIM: Automatic Interaction Machine for Click-Through Rate PredictionCode1
Retrieval & Interaction Machine for Tabular Data PredictionCode1
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR PredictionCode1
FINT: Field-aware INTeraction Neural Network For CTR PredictionCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate PredictionCode1
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate PredictionCode1
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information NetworkCode1
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait IssueCode1
BARS-CTR: Open Benchmarking for Click-Through Rate PredictionCode1
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank SystemsCode1
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate PredictionCode1
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate PredictionCode1
User Behavior Retrieval for Click-Through Rate PredictionCode1
Deep Interest with Hierarchical Attention Network for Click-Through Rate PredictionCode1
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate PredictionCode1
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad ServingCode1
Interpretable Click-Through Rate Prediction through Hierarchical AttentionCode1
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR PredictionCode1
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionCode1
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID EmbeddingsCode1
Knowledge Graph Convolutional Networks for Recommender SystemsCode1
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsCode1
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender SystemsCode1
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at EtsyCode1
Deep & Cross Network for Ad Click PredictionsCode1
Deep Interest Network for Click-Through Rate PredictionCode1
DeepFM: A Factorization-Machine based Neural Network for CTR PredictionCode1
Wide & Deep Learning for Recommender SystemsCode1
Generative Click-through Rate Prediction with Applications to Search Advertising—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify—0
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert SpecializationCode0
DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR PredictionCode0
Field Matters: A lightweight LLM-enhanced Method for CTR Prediction—0
Feature Staleness Aware Incremental Learning for CTR PredictionCode0
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR ChallengeCode0
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction—0
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Scaled Supervision is an Implicit Lipschitz RegularizerCode0
Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction—0
Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders—0
LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction—0
Towards An Efficient LLM Training Paradigm for CTR Prediction—0
Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling—0
A Universal Framework for Compressing Embeddings in CTR PredictionCode0
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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