2 papers
cs.DB2026
USDB: Scaling Storage for Next-Generation Recommender Systems at ByteDance
Xiaoran Zhu, Jieming Cai, Weiping Yu +11
Recommendation models consume increasingly long, attribute-rich user histories, making small continuous updates and repeated large reads a central storage asymmetry. We present USD…
cs.IR2025
LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
Zheng Chai, Qin Ren, Xijun Xiao +14
Modeling ultra-long user behavior sequences is critical for capturing both long- and short-term preferences in industrial recommender systems. Existing solutions typically rely on…