5 papers
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…
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…
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…
Collaborative Interest-aware Graph Learning for Group Identification
Rui Zhao, Beihong Jin, Beibei Li +1
With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI…
Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation
Beibei Li, Tao Xiang, Beihong Jin +2
Variational AutoEncoder (VAE) for Sequential Recommendation (SR), which learns a continuous distribution for each user-item interaction sequence rather than a determinate embedding…