Efficient Dataset Selection for Continual Adaptation of Generative Recommenders

Cathy Jiao; Juan Elenter; Praveen Ravichandran; Bernd Huber; Joseph Cauteruccio; Todd Wasson; Timothy Heath; Chenyan Xiong; Mounia Lalmas; Paul Bennett

arXiv · 2604.07739

Efficient Dataset Selection for Continual Adaptation of Generative Recommenders

Cathy Jiao·Juan Elenter·Praveen Ravichandran·Bernd Huber·Joseph Cauteruccio·Todd Wasson·Timothy Heath·Chenyan Xiong·Mounia Lalmas·Paul Bennett

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Abstract

Recommendation systems must continuously adapt to evolving user behavior, yet the volume of data generated in large-scale streaming environments makes frequent full retraining impractical. This work investigates how targeted data selection can mitigate performance degradation caused by temporal distributional drift while maintaining scalability. We evaluate a range of representation choices and sampling strategies for curating small but informative subsets of user interaction data. Our results demonstrate that gradient-based representations, coupled with distribution-matching, improve downstream model performance, achieving training efficiency gains while preserving robustness to drift. These findings highlight data curation as a practical mechanism for scalable monitoring and adaptive model updates in production-scale recommendation systems.

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Cathy Jiao, Juan Elenter, Praveen Ravichandran, Bernd Huber, Joseph Cauteruccio, Todd Wasson, Timothy Heath, Chenyan Xiong, Mounia Lalmas, Paul Bennett. 2026-04-09. Efficient Dataset Selection for Continual Adaptation of Generative Recommenders. https://arxiv.org/abs/2604.07739

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