高效数据选择用于生成推荐系统的持续适应
Efficient Dataset Selection for Continual Adaptation of Generative Recommenders
AI总结：
研究通过针对性数据选择缓解时间分布漂移导致的性能下降，采用梯度表示与分布匹配提升模型性能，实现训练效率提升与鲁棒性保障。
摘要：
推荐系统必须持续适应变化的用户行为，但大规模流式环境中生成的数据量使得频繁全量重训练不现实。本文探讨如何通过针对性数据选择减轻因时间分布漂移导致的性能下降，同时保持可扩展性。我们评估了多种表示选择和采样策略，用于精选小但有信息量的用户交互数据子集。结果表明，结合梯度表示与分布匹配的方法提升了下游模型性能，在保持鲁棒性的同时实现了训练效率的提升。这些发现突显了数据整理作为大规模推荐系统中可扩展监控和自适应模型更新的实用机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
