4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.IR2026
Unleashing the Potential of Sparse Attention on Long-term Behaviors for CTR Prediction
Weijiang Lai, Beihong Jin, Di Zhang +5
In recent years, the success of large language models (LLMs) has driven the exploration of scaling laws in recommender systems. However, models that demonstrate scaling laws are ac…
cs.IR2025★ 4 cited
Exploring Scaling Laws of CTR Model for Online Performance Improvement
Weijiang Lai, Beihong Jin, Jiongyan Zhang +5
CTR models play a vital role in improving user experience and boosting business revenue in many online personalized services. However, current CTR models generally encounter bottle…
cs.IR2025★ 2 cited
Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction
Weijiang Lai, Beihong Jin, Yapeng Zhang +5
CTR (Click-Through Rate) prediction, crucial for recommender systems and online advertising, etc., has been confirmed to benefit from modeling long-term user behaviors. Nonetheless…