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Click-Through Rate Prediction
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Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
<span style="color:grey; opacity: 0.6">( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation
An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
Field Matters: A lightweight LLM-enhanced Method for CTR Prediction
1$^{st}$ Place Solution of WWW 2025 EReL@MIR Workshop Multimodal CTR Prediction Challenge
Feature Staleness Aware Incremental Learning for CTR Prediction
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation
Scaled Supervision is an Implicit Lipschitz Regularizer
Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction
Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders
LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction
Towards An Efficient LLM Training Paradigm for CTR Prediction
Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
A Universal Framework for Compressing Embeddings in CTR Prediction
Context-Aware Lifelong Sequential Modeling for Online Click-Through Rate Prediction
MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling
General Information Metrics for Improving AI Model Training Efficiency
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction
Explainable CTR Prediction via LLM Reasoning
Ensemble Learning via Knowledge Transfer for CTR Prediction
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model
Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale Click-Through Rate Prediction at Taobao
A Collaborative Ensemble Framework for CTR Prediction
Towards Unifying Feature Interaction Models for Click-Through Rate Prediction
All-domain Moveline Evolution Network for Click-Through Rate Prediction
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