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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

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Showing 1–10 of 391 papers

TitleStatusHype
Generative Click-through Rate Prediction with Applications to Search Advertising—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify—0
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 Prediction—0
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
1STECAUC0.97—Unverified
2KNIAUC0.94—Unverified
3RippleNetAUC0.92—Unverified
4MKRAUC0.92—Unverified
5DCNv3AUC0.91—Unverified
6AutoIntAUC0.85—Unverified