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 110 of 391 papers

TitleStatusHype
Generative Click-through Rate Prediction with Applications to Search Advertising0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation0
An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify0
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
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate PredictionCode1
Field Matters: A lightweight LLM-enhanced Method for CTR Prediction0
1^st Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction ChallengeCode1
Feature Staleness Aware Incremental Learning for CTR PredictionCode0
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR ChallengeCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1xDeepFMAUC0.84Unverified
2Wide & DeepAUC0.84Unverified
3DeepFMAUC0.84Unverified
4PNNAUC0.83Unverified
5RippleNetAUC0.68Unverified
6DKNAUC0.66Unverified
7DNNAUC0.03Unverified