4 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Beibei Li, Yiyuan Zheng, Beihong Jin +3
Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models…
Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching
Beibei Li, Beihong Jin, Yisong Yu +4
Watching micro-videos is becoming a part of public daily life. Usually, user watching behaviors are thought to be rooted in their multiple different interests. In the paper, we pro…