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 151–200 of 391 papers

TitleStatusHype
xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactionsCode0
DKN: Deep Knowledge-Aware Network for News RecommendationCode0
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
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction—0
MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling—0
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction—0
Mitigate Position Bias with Coupled Ranking Bias on CTR Prediction—0
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction—0
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction—0
Moment&Cross: Next-Generation Real-Time Cross-Domain CTR Prediction for Live-Streaming Recommendation at Kuaishou—0
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction—0
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models—0
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction—0
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction—0
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System—0
Mutual Learning for Finetuning Click-Through Rate Prediction Models—0
NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction—0
News Popularity Beyond the Click-Through-Rate for Personalized Recommendations—0
On-Device Model Fine-Tuning with Label Correction in Recommender Systems—0
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction—0
Online Interaction Detection for Click-Through Rate Prediction—0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction—0
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction—0
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction—0
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System—0
PHN: Parallel heterogeneous network with soft gating for CTR prediction—0
Polyhedral Conic Classifier for CTR Prediction—0
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction—0
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction—0
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising—0
Predicting clicks in online display advertising with latent features and side-information—0
Provable Sparse Tensor Decomposition—0
RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks—0
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao—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