Efficient Dataset Selection for Continual Adaptation of Generative Recommenders

• URL: http://arxiv.org/abs/2604.07739v1
• Date: Thu, 09 Apr 2026 02:48:52 GMT
• Title: Efficient Dataset Selection for Continual Adaptation of Generative Recommenders
• Abstract summary: This work investigates how targeted data selection can mitigate performance degradation caused by temporal distributional drift.<n>We evaluate a range of representation choices and sampling strategies for curating small but informative subsets of user interaction data.
• Score: 19.8068072624233
• License: http://creativecommons.org/licenses/by/4.0/
• 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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