2 papers
cs.IR2026
IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems
Xinchun Li, Ning Zhang, Qianqian Yang +11
Although sophisticated sequence modeling paradigms have achieved remarkable success in recommender systems, the information capacity of hand-crafted sequential features constrains…
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…