3 papers
cs.IR2026
Teacher Retains Full Tokens, Student Merges Efficiently: TM20K for E-Commerce Sequence Modeling in Ad Recommendation
Xinchun Li, Duoru Zheng, Wenlin Zhao +13
Benefiting from ultra-long behavior sequence modeling, existing recommender systems bring users a better experience via simultaneously considering their long-term and short-term in…
cs.IR2026
Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Haoran Ding, Wenlin Zhao, Yuchen Jiang +16
Large recommendation models have demonstrated substantial potential gains under scaling laws, yet these gains are difficult to realize in industrial recommendation systems because…
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…