5 citations · 6 across the 11 of their papers we have counts for
6 papers · 1 filter
VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling
Kaiyuan Li, Yongxiang Tang, Yanhua Cheng +5
In large-scale recommender systems, ultra-long user behavior sequences encode rich signals of evolving interests. Extending sequence length generally improves accuracy, but directl…
Aggregate and Broadcast: Scalable and Efficient Feature Interaction for Recommender Systems
Kaiyuan Li, Yongxiang Tang, Wenzheng Shu +5
Feature interaction is a core ingredient in ranking models for large-scale recommender systems, yet making it both expressive and efficiently scalable remains challenging. Exhausti…
Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems
Wenzheng Shu, Yanxiang Zeng, Yongxiang Tang +6
Offline reinforcement learning (RL) has emerged as a prevalent and effective methodology for real-world recommender systems, enabling learning policies from historical data and cap…
CHIME: A Compressive Framework for Holistic Interest Modeling
Yong Bai, Rui Xiang, Kaiyuan Li +5
Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with…
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
Kaiyuan Li, Rui Xiang, Yong Bai +5
Multi-modal sequential recommendation systems leverage auxiliary signals (e.g., text, images) to alleviate data sparsity in user-item interactions. While recent methods exploit lar…
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain
Rui Xia, Yanhua Cheng, Yongxiang Tang +4
Recovering user preferences from user-item interaction matrices is a key challenge in recommender systems. While diffusion models can sample and reconstruct preferences from latent…