Cover Image

Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Xu, Cheng, Zida, Yao, Jiangchao, Ju, Chen, Huang, Weilin, Lan, Jinsong, Zeng, Xiaoyi, Xiao, Shuai
Format:	Preprint
Published:	2023
Subjects:	Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
by: Chen, Xu, et al.
Published: (2024)

Category-Oriented Representation Learning for Image to Multi-Modal Retrieval
by: Cheng, Zida, et al.
Published: (2023)

Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
by: Huang, Junjie, et al.
Published: (2024)

Optimizing Feature Set for Click-Through Rate Prediction
by: Lyu, Fuyuan, et al.
Published: (2023)

Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction
by: Zhang, Moyu, et al.
Published: (2025)

All-domain Moveline Evolution Network for Click-Through Rate Prediction
by: Gao, Chen, et al.
Published: (2024)

Towards Unifying Feature Interaction Models for Click-Through Rate Prediction
by: Kang, Yu, et al.
Published: (2024)

Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
by: Zhang, Moyu, et al.
Published: (2026)

Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
by: Hou, Ruijie, et al.
Published: (2023)

Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction
by: Liu, Qi, et al.
Published: (2024)

Open Benchmarking for Click-Through Rate Prediction
by: Zhu, Jieming, et al.
Published: (2020)

Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
by: Jin, Jinqiu, et al.
Published: (2024)

Enhancing Sequential Recommendation with World Knowledge from Large Language Models
by: Dai, Tianjie, et al.
Published: (2025)

Distribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction
by: Liu, Jiahao, et al.
Published: (2026)

FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
by: Li, Honghao, et al.
Published: (2024)

Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation
by: Cui, Jiajun, et al.
Published: (2024)

GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm
by: Chen, Shaopeng, et al.
Published: (2026)

FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
by: Dai, Zenan, et al.
Published: (2026)

Retrieval-Oriented Knowledge for Click-Through Rate Prediction
by: Liu, Huanshuo, et al.
Published: (2024)

Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation
by: Qin, Jiangcheng, et al.
Published: (2025)

A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features
by: Wang, Hao, et al.
Published: (2024)

Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
by: Fan, Zhongxiang, et al.
Published: (2024)

Deep Pattern Network for Click-Through Rate Prediction
by: Zhang, Hengyu, et al.
Published: (2024)

Context-Aware Lifelong Sequential Modeling for Online Click-Through Rate Prediction
by: Guo, Ting, et al.
Published: (2025)

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
by: Zeng, Zhichen, et al.
Published: (2024)

RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
by: Li, Yushen, et al.
Published: (2024)

LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
by: Tang, Jiakai, et al.
Published: (2026)

Quadratic Interest Network for Multimodal Click-Through Rate Prediction
by: Li, Honghao, et al.
Published: (2025)

EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
by: Liu, Mingyang, et al.
Published: (2026)

A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model
by: Yang, Hao, et al.
Published: (2024)

Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
by: Zhao, Qihang, et al.
Published: (2025)

NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction
by: Aksu, Dogukan, et al.
Published: (2024)

Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
by: Lv, Yimin, et al.
Published: (2026)

FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction
by: Ouyang, Wentao, et al.
Published: (2024)

Mutual Learning for Finetuning Click-Through Rate Prediction Models
by: Yilmaz, Ibrahim Can, et al.
Published: (2024)

ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
by: Dong, Rui, et al.
Published: (2025)

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
by: Fan, Alin, et al.
Published: (2025)

DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
by: Zhang, Moyu, et al.
Published: (2025)

Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
by: Zhou, Chang, et al.
Published: (2024)

Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
by: Li, Shiwei, et al.
Published: (2022)