Saved in:
| Main Authors: | Dong, Rui, Ouyang, Wentao, Liu, Xiangzheng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.07594 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction
by: Ouyang, Wentao, et al.
Published: (2024)
by: Ouyang, Wentao, et al.
Published: (2024)
Optimizing Feature Set for Click-Through Rate Prediction
by: Lyu, Fuyuan, et al.
Published: (2023)
by: Lyu, Fuyuan, et al.
Published: (2023)
Deep Pattern Network for Click-Through Rate Prediction
by: Zhang, Hengyu, et al.
Published: (2024)
by: Zhang, Hengyu, et al.
Published: (2024)
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
by: Lv, Yimin, et al.
Published: (2026)
by: Lv, Yimin, et al.
Published: (2026)
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction
by: Liu, Qi, et al.
Published: (2024)
by: Liu, Qi, et al.
Published: (2024)
EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
by: Liu, Mingyang, et al.
Published: (2026)
by: Liu, Mingyang, et al.
Published: (2026)
Open Benchmarking for Click-Through Rate Prediction
by: Zhu, Jieming, et al.
Published: (2020)
by: Zhu, Jieming, et al.
Published: (2020)
Towards Unifying Feature Interaction Models for Click-Through Rate Prediction
by: Kang, Yu, et al.
Published: (2024)
by: Kang, Yu, et al.
Published: (2024)
All-domain Moveline Evolution Network for Click-Through Rate Prediction
by: Gao, Chen, et al.
Published: (2024)
by: Gao, Chen, et al.
Published: (2024)
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation
by: Qin, Jiangcheng, et al.
Published: (2025)
by: Qin, Jiangcheng, et al.
Published: (2025)
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
by: Liu, Huanshuo, et al.
Published: (2024)
by: Liu, Huanshuo, et al.
Published: (2024)
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
by: Li, Honghao, et al.
Published: (2025)
by: Li, Honghao, et al.
Published: (2025)
Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
by: Fan, Alin, et al.
Published: (2025)
by: Fan, Alin, et al.
Published: (2025)
Mutual Learning for Finetuning Click-Through Rate Prediction Models
by: Yilmaz, Ibrahim Can, et al.
Published: (2024)
by: Yilmaz, Ibrahim Can, et al.
Published: (2024)
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
by: Li, Shiwei, et al.
Published: (2022)
by: Li, Shiwei, et al.
Published: (2022)
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
by: Tang, Jiakai, et al.
Published: (2026)
by: Tang, Jiakai, et al.
Published: (2026)
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
by: Li, Yushen, et al.
Published: (2024)
by: Li, Yushen, et al.
Published: (2024)
Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model
by: Ouyang, Wentao, et al.
Published: (2024)
by: Ouyang, Wentao, et al.
Published: (2024)
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
by: Jin, Jinqiu, et al.
Published: (2024)
by: Jin, Jinqiu, et al.
Published: (2024)
Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction
by: Wei, Zhuoxing, et al.
Published: (2025)
by: Wei, Zhuoxing, et al.
Published: (2025)
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
by: Hou, Ruijie, et al.
Published: (2023)
by: Hou, Ruijie, et al.
Published: (2023)
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
by: Huang, Junjie, et al.
Published: (2024)
by: Huang, Junjie, et al.
Published: (2024)
Generative Long-term User Interest Modeling for Click-Through Rate Prediction
by: Shao, Jiangli, et al.
Published: (2026)
by: Shao, Jiangli, et al.
Published: (2026)
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
by: Li, Honghao, et al.
Published: (2024)
by: Li, Honghao, et al.
Published: (2024)
Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
by: Zhao, Qihang, et al.
Published: (2025)
by: Zhao, Qihang, et al.
Published: (2025)
GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm
by: Chen, Shaopeng, et al.
Published: (2026)
by: Chen, Shaopeng, et al.
Published: (2026)
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
by: Chen, Xu, et al.
Published: (2023)
by: Chen, Xu, et al.
Published: (2023)
FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
by: Dai, Zenan, et al.
Published: (2026)
by: Dai, Zenan, et al.
Published: (2026)
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
by: Fan, Zhongxiang, et al.
Published: (2024)
by: Fan, Zhongxiang, et al.
Published: (2024)
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction
by: Wang, Yule, et al.
Published: (2021)
by: Wang, Yule, et al.
Published: (2021)
Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
by: Chen, Xu, et al.
Published: (2024)
by: Chen, Xu, et al.
Published: (2024)
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
by: Zhou, Chang, et al.
Published: (2024)
by: Zhou, Chang, et al.
Published: (2024)
Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction
by: Zhang, Moyu, et al.
Published: (2025)
by: Zhang, Moyu, et al.
Published: (2025)
Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
by: Zhu, Wenqiao, et al.
Published: (2025)
by: Zhu, Wenqiao, et al.
Published: (2025)
From Collapse to Stability: A Knowledge-Driven Ensemble Framework for Scaling Up Click-Through Rate Prediction Models
by: Li, Honghao, et al.
Published: (2024)
by: Li, Honghao, et al.
Published: (2024)
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
by: Zeng, Zhichen, et al.
Published: (2024)
by: Zeng, Zhichen, et al.
Published: (2024)
Distribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction
by: Liu, Jiahao, et al.
Published: (2026)
by: Liu, Jiahao, et al.
Published: (2026)
DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
by: Zhang, Moyu, et al.
Published: (2025)
by: Zhang, Moyu, et al.
Published: (2025)
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
by: Zhang, Kexin, et al.
Published: (2024)
by: Zhang, Kexin, et al.
Published: (2024)
NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction
by: Aksu, Dogukan, et al.
Published: (2024)
by: Aksu, Dogukan, et al.
Published: (2024)
Similar Items
-
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction
by: Ouyang, Wentao, et al.
Published: (2024) -
Optimizing Feature Set for Click-Through Rate Prediction
by: Lyu, Fuyuan, et al.
Published: (2023) -
Deep Pattern Network for Click-Through Rate Prediction
by: Zhang, Hengyu, et al.
Published: (2024) -
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
by: Lv, Yimin, et al.
Published: (2026) -
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction
by: Liu, Qi, et al.
Published: (2024)