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
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data—0
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction—0
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving—0
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling—0
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction—0
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction—0
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation—0
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation—0
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training—0
Single-shot Embedding Dimension Search in Recommender System—0
SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction—0
Soft Retargeting Network for Click Through Rate Prediction—0
Sparse Tensor Additive Regression—0
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services—0
Star+: A New Multi-Domain Model for CTR Prediction—0
STEC: See-Through Transformer-based Encoder for CTR Prediction—0
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data—0
TBIN: Modeling Long Textual Behavior Data for CTR Prediction—0
Temporal Importance Factor for Loss Functions for CTR Prediction—0
TFNet: Multi-Semantic Feature Interaction for CTR Prediction—0
The Effects of Data Split Strategies on the Offline Experiments for CTR Prediction—0
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction—0
Towards An Efficient LLM Training Paradigm for CTR Prediction—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Towards Personality-Aware Recommendation—0
Towards Practical Second Order Optimization for Deep Learning—0
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions—0
Towards Unifying Feature Interaction Models for Click-Through Rate Prediction—0
Training with Multi-Layer Embeddings for Model Reduction—0
TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity—0
TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou—0
Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models—0
U-rank: Utility-oriented Learning to Rank with Implicit Feedback—0
Using Neural Networks for Click Prediction of Sponsored Search—0
Visual Encoding and Debiasing for CTR Prediction—0
Visualizing and Understanding Deep Neural Networks in CTR Prediction—0
v-TCM: Vertical-aware Transformer Click Model for Web Search—0
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction—0
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction—0
A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second—0
A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features—0
A Collaborative Ensemble Framework for CTR Prediction—0
A Collaborative Transfer Learning Framework for Cross-domain Recommendation—0
AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction—0
AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction—0
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction—0
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction—0
Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling—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