AI 中文总结
针对推荐系统，提出Obliviate两阶段遗忘框架。第一阶段用低秩遗忘适配器，第二阶段用局部感知校准，能在保持推荐质量的同时高效遗忘交互数据，降低计算成本，为大规模推荐系统提供实用方案。
AI 中文摘要
在数据隐私法规背景下，机器遗忘在直接基于用户交互数据训练的推荐系统中愈发关键。本文旨在从训练模型中移除请求的交互数据及其下游影响，同时保持推荐质量，且不产生完全重新训练的巨大计算成本。现有方法存在局限性。本文提出Obliviate，一个高效的两阶段遗忘框架。第一阶段引入低秩遗忘适配器（LUA），利用轻量级海森矩阵代理实现曲率感知和高效遗忘；第二阶段提出局部感知校准（LAC），通过基于排序的目标强制遗忘并通过知识蒸馏保持效用。大量实证评估表明，Obliviate在推荐质量损失最小的情况下实现了高水平遗忘，且计算成本显著降低，为大规模推荐系统提供了实用且可扩展的解决方案。
英文摘要
Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interaction data. The goal of this work is to remove requested interaction data and their downstream influence from trained model while preserving recommendation quality, and to do so without incurring the substantial computational cost of full retraining. Existing approaches exhibit several limitations, including limited unlearning completeness and degradation in recommendation performance, while having substantial computational overhead. In this paper, we propose Obliviate, an efficient two-stage unlearning framework for recommender systems that achieves high unlearning completeness while maintaining good utility. In the first stage, we introduce a Low-Rank Unlearning Adapter (LUA), which employs a lightweight Hessian proxy to enable curvature-aware and efficient unlearning through localized low-rank adapters rather than full parameters. In the second stage, we propose Locality-Aware Calibration (LAC), a lightweight refinement stage that updates only the adapter parameters to improve the performance by enforcing unlearning via ranking-based objectives while preserving utility through knowledge distillation. Extensive empirical evaluations demonstrate that Obliviate achieves high level of forgetting with minimal loss in recommendation quality and at significantly reduced computational cost, offering a practical and scalable solution for large-scale recommender systems.
CommentsAccepted at ICML 2026
