3 papers
cs.AI2026
Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking
Zhimeng Jiang, Yuening Li, Ruixiao Sun +4
Industrial recommendation rankers keep scaling in width and depth to billions of parameters, but this increasingly incurs costs in parameter growth, distributed communication overh…
cs.IR2026
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective
Yuening Li, Zeyu Wang, Zhen Zhang +8
Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions, faces critical stability chal…
cs.IR2026
Soft Curriculum Learning for Optimizing Fresh and Generalized Recommendations
Arnab Bhadury, Siyan Zheng, Anlan Yu +6
Large-scale recommender systems, particularly short-form video platforms, are often bottlenecked by massive popularity feedback loops. In such environments, as models recommend pop…