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 301–350 of 391 papers

TitleStatusHype
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model—0
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction—0
KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction—0
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding—0
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction—0
Learning a Product Relevance Model from Click-Through Data in E-Commerce—0
Learning Feature Interactions with Lorentzian Factorization Machine—0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction—0
Learning Representations of Categorical Feature Combinations via Self-Attention—0
Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data—0
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation—0
LIBER: Lifelong User Behavior Modeling Based on Large Language Models—0
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search—0
LiRank: Industrial Large Scale Ranking Models at LinkedIn—0
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions—0
Looking at CTR Prediction Again: Is Attention All You Need?—0
Look into the Future: Deep Contextualized Sequential Recommendation—0
NCS4CVR: Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction—0
Making the Full Model Adaptive: Multi-level Domain Adaptation for Multi-Domain CTR Prediction—0
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer—0
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation—0
GateNet: Gating-Enhanced Deep Network for Click-Through Rate PredictionCode0
Deep Character-Level Click-Through Rate Prediction for Sponsored SearchCode0
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual RecommendationCode0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light NetCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate PredictionCode0
Understanding and Counteracting Feature-Level Bias in Click-Through Rate PredictionCode0
DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR PredictionCode0
FinalMLP: An Enhanced Two-Stream MLP Model for CTR PredictionCode0
Scaled Supervision is an Implicit Lipschitz RegularizerCode0
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display AdvertisingCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR PredictionCode0
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate PredictionCode0
Hybrid CNN Based Attention with Category Prior for User Image Behavior ModelingCode0
Click-Through Rate Prediction with the User Memory NetworkCode0
An Embedding Learning Framework for Numerical Features in CTR PredictionCode0
CELA: Cost-Efficient Language Model Alignment for CTR PredictionCode0
DKN: Deep Knowledge-Aware Network for News RecommendationCode0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactionsCode0
Field-Embedded Factorization Machines for Click-through rate predictionCode0
Differentiable NAS Framework and Application to Ads CTR PredictionCode0
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
Deep Spatio-Temporal Neural Networks for Click-Through Rate PredictionCode0
An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced RecommendationCode0
Sparse Attentive Memory Network for Click-through Rate Prediction with Long SequencesCode0
Show:102550
← PrevPage 7 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
9STECAUC0.81—Unverified
10DNN + MMBAttnAUC0.81—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
5DeepFMAUC0.85—Unverified
6DCNv2AUC0.85—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
2FinalMLPAUC0.99—Unverified
3FinalMLP + MMBAttnAUC0.99—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