3 papers
cs.IR2026
GRP v0.1 Technical Report
Wenfeng Zhuo, Vincent Xue, Charles Wei +19
Industrial recommendation systems rely on multi-stage cascades whose retrieval, ranking, and serving components are difficult to replace jointly. We present GRP, a generative recom…
cs.LG2026
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
Dingsu Wang, Filip Ryzner, Kelly He +17
As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on lo…
cs.IR2026
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
Yichu Zhou, Mehdi Ben Ayed, Lin Yang +9
Large-scale recommenders encode multi-objective trade-offs by combining multiple predicted outcomes into a single utility score. Although this utility layer can be updated independ…