3 papers
cs.IR2026
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
cs.IR2026
Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation
Ge Fan, Nan Zhao, Kai Meng +6
With the rapid evolution of internet services, recommendation systems have become indispensable. In particular, the blending (re-ranking) stage plays a pivotal role in allocating t…
cs.IR2026
A Long-term Value Prediction Framework In Video Ranking
Huabin Chen, Xinao Wang, Huiping Chu +5
Accurately modeling long-term value (LTV) at the ranking stage of short-video recommendation remains challenging. While delayed feedback and extended engagement have been explored,…