CETN: Contrast-enhanced Through Network for CTR Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Honghao, Sang, Lei, Zhang, Yi, Zhang, Xuyun, Zhang, Yiwen |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
by: Li, Honghao, et al.
Published: (2024)
by: Li, Honghao, et al.
Published: (2024)
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
by: Sang, Lei, et al.
Published: (2024)
by: Sang, Lei, et al.
Published: (2024)
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)
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
by: Li, Honghao, et al.
Published: (2025)
by: Li, Honghao, 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)
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
by: Li, Honghao, et al.
Published: (2025)
by: Li, Honghao, et al.
Published: (2025)
Dual-domain Collaborative Denoising for Social Recommendation
by: Chen, Wenjie, et al.
Published: (2024)
by: Chen, Wenjie, et al.
Published: (2024)
Generative-Contrastive Heterogeneous Graph Neural Network
by: Wang, Yu, et al.
Published: (2024)
by: Wang, Yu, et al.
Published: (2024)
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
by: Zhang, Yu, et al.
Published: (2025)
by: Zhang, Yu, et al.
Published: (2025)
Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation
by: Sang, Lei, et al.
Published: (2025)
by: Sang, Lei, et al.
Published: (2025)
Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation
by: Sang, Lei, et al.
Published: (2024)
by: Sang, Lei, et al.
Published: (2024)
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
by: Zhang, Yi, et al.
Published: (2024)
by: Zhang, Yi, et al.
Published: (2024)
DIAURec: Dual-Intent Space Representation Optimization for Recommendation
by: Zhang, Yu, et al.
Published: (2026)
by: Zhang, Yu, et al.
Published: (2026)
Intent Representation Learning with Large Language Model for Recommendation
by: Wang, Yu, et al.
Published: (2025)
by: Wang, Yu, 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)
by: Zhang, Moyu, et al.
Published: (2025)
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)
Fusion Self-supervised Learning for Recommendation
by: Zhang, Yu, et al.
Published: (2024)
by: Zhang, Yu, et al.
Published: (2024)
Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction
by: Lai, Weijiang, et al.
Published: (2025)
by: Lai, Weijiang, et al.
Published: (2025)
Explainable CTR Prediction via LLM Reasoning
by: Yu, Xiaohan, et al.
Published: (2024)
by: Yu, Xiaohan, et al.
Published: (2024)
MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
by: Xiao, Yutian, et al.
Published: (2025)
by: Xiao, Yutian, et al.
Published: (2025)
STEC: See-Through Transformer-based Encoder for CTR Prediction
by: Dilbaz, Serdarcan, et al.
Published: (2023)
by: Dilbaz, Serdarcan, et al.
Published: (2023)
Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction
by: Cui, Yu, et al.
Published: (2025)
by: Cui, Yu, et al.
Published: (2025)
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
by: Yang, Zhiming, et al.
Published: (2024)
by: Yang, Zhiming, et al.
Published: (2024)
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
by: Wang, Fangye, et al.
Published: (2023)
by: Wang, Fangye, et al.
Published: (2023)
Enhancing CTR Prediction with De-correlated Expert Networks
by: Wang, Jiancheng, et al.
Published: (2025)
by: Wang, Jiancheng, et al.
Published: (2025)
Unleashing the Potential of Sparse Attention on Long-term Behaviors for CTR Prediction
by: Lai, Weijiang, et al.
Published: (2026)
by: Lai, Weijiang, et al.
Published: (2026)
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
by: Lin, Jianghao, et al.
Published: (2023)
by: Lin, Jianghao, et al.
Published: (2023)
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework
by: Han, Ruidong, et al.
Published: (2024)
by: Han, Ruidong, et al.
Published: (2024)
Mitigate Position Bias with Coupled Ranking Bias on CTR Prediction
by: Zhao, Yao, et al.
Published: (2024)
by: Zhao, Yao, et al.
Published: (2024)
MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths
by: Zhang, Moyu, et al.
Published: (2025)
by: Zhang, Moyu, et al.
Published: (2025)
LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction
by: Song, Xin, et al.
Published: (2025)
by: Song, Xin, et al.
Published: (2025)
DUET: Dual Model Co-Training for Entire Space CTR Prediction
by: Xiao, Yutian, et al.
Published: (2025)
by: Xiao, Yutian, et al.
Published: (2025)
FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
by: Zhang, Jun, et al.
Published: (2026)
by: Zhang, Jun, et al.
Published: (2026)
From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
by: Lu, Ziang, et al.
Published: (2026)
by: Lu, Ziang, et al.
Published: (2026)
Feature Staleness Aware Incremental Learning for CTR Prediction
by: Wang, Zhikai, et al.
Published: (2025)
by: Wang, Zhikai, et al.
Published: (2025)
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
by: Xiao, Zhibo, et al.
Published: (2024)
by: Xiao, Zhibo, et al.
Published: (2024)
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
by: Xu, Xiang, et al.
Published: (2024)
by: Xu, Xiang, et al.
Published: (2024)
R2LED: Equipping Retrieval and Refinement in Lifelong User Modeling with Semantic IDs for CTR Prediction
by: Liu, Qidong, et al.
Published: (2026)
by: Liu, Qidong, et al.
Published: (2026)
AIE: Auction Information Enhanced Framework for CTR Prediction in Online Advertising
by: Yang, Yang, et al.
Published: (2024)
by: Yang, Yang, et al.
Published: (2024)
MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems
by: Zhang, Yi, et al.
Published: (2025)
by: Zhang, Yi, et al.
Published: (2025)
Similar Items
-
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
by: Li, Honghao, et al.
Published: (2024) -
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
by: Sang, Lei, et al.
Published: (2024) -
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
by: Li, Honghao, et al.
Published: (2024) -
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
by: Li, Honghao, 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)