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 201–250 of 391 papers

TitleStatusHype
PHN: Parallel heterogeneous network with soft gating for CTR prediction—0
Soft Retargeting Network for Click Through Rate Prediction—0
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate PredictionCode1
HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction—0
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction—0
BARS: Towards Open Benchmarking for Recommender SystemsCode2
Visual Encoding and Debiasing for CTR Prediction—0
v-TCM: Vertical-aware Transformer Click Model for Web Search—0
Hybrid CNN Based Attention with Category Prior for User Image Behavior ModelingCode0
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction—0
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction—0
Enhancing CTR Prediction with Context-Aware Feature Representation LearningCode1
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPUCode1
Single-shot Embedding Dimension Search in Recommender System—0
On the Adaptation to Concept Drift for CTR Prediction—0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender SystemsCode0
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction—0
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction—0
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce SearchCode1
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction—0
Click-Through Rate Prediction in Online Advertising: A Literature Review—0
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate PredictionCode1
Real World Large Scale Recommendation Systems Reproducibility and Smooth ActivationsCode1
Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data—0
Triangle Graph Interest Network for Click-through Rate PredictionCode1
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced RecommendationCode1
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer—0
Continual Learning for CTR Prediction: A Hybrid Approach—0
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework—0
GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks—0
Communication-Efficient TeraByte-Scale Model Training Framework for Online Advertising—0
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate PredictionCode0
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction—0
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers—0
A General Framework for Debiasing in CTR Prediction—0
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction—0
PaddleRecCode2
Dynamic Parameterized Network for CTR Prediction—0
AIM: Automatic Interaction Machine for Click-Through Rate PredictionCode1
Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models—0
Differentiable NAS Framework and Application to Ads CTR PredictionCode0
AEFE: Automatic Embedded Feature Engineering for Categorical Features—0
Feature Shapley: A general framework to discovering useful feature interactions—0
CareGraph: A Graph-based Recommender System for Diabetes Self-Care—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation—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