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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…
cs.IR2026
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
Dongqi Fu, Kaushik Rangadurai, Haiyu Lu +13
The increase in data volume, computational resources, and model parameters during training has led to the development of numerous large-scale industrial retrieval models for recomm…