Efficient Dataset Selection for Continual Adaptation of Generative Recommenders
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. T...
| Authors | Cathy Jiao, Juan Elenter, Praveen Ravichandran et al. |
|---|---|
| Primary category | cs.IR |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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