Efficient Dataset Selection for Continual Adaptation of Generative Recommenders

2026-04-08

AI Summary

This work addresses the high computational cost of full retraining and the challenge of adapting to user behavior drift in large-scale streaming recommender systems by proposing an efficient continual learning framework. The approach integrates gradient-informed user interaction representations with a distribution-matching sampling strategy to intelligently select a small, informative subset of data for model updates. Experimental results demonstrate that this method substantially reduces training overhead while effectively enhancing model robustness against temporal distribution shifts, thereby validating the practical utility and scalability of intelligent data selection in industrial-scale recommendation systems.

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.

Problem

• continual adaptation
• generative recommenders
• distributional drift
• dataset selection
• scalability

Innovation

• data selection
• continual adaptation
• distributional drift
• gradient-based representation
• generative recommenders