LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction

Documenti analoghi

• STORE: Semantic Tokenization, Orthogonal Rotation and Efficient Attention for Scaling Up Ranking Models
  di: Xu, Yi, et al.
  Pubblicazione: (2025)
• MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths
  di: Zhang, Moyu, et al.
  Pubblicazione: (2025)
• Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction
  di: Xu, Yi, et al.
  Pubblicazione: (2025)
• Unleashing the Potential of Sparse Attention on Long-term Behaviors for CTR Prediction
  di: Lai, Weijiang, et al.
  Pubblicazione: (2026)
• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
  di: Xu, Xiang, et al.
  Pubblicazione: (2024)