SURF：用于推荐遗忘的减法更新
SURF: Subtractive Updates for Recommender Forgetting
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中文总结 AI 辅助
SURF提出了一种轻量级框架，通过识别嵌入空间邻域、训练辅助模型并在推理时减去其分数，实现序列推荐系统的高效近似机器遗忘，在降低计算成本的同时保持遗忘效果，NDCG@20提升32%。
中文摘要 AI 辅助
用户隐私需求的日益增长以及GDPR等法规的合规要求，使得机器遗忘成为现代推荐系统的一项基本要求。然而，序列推荐系统（SRS）由于其依赖于时间交互模式，在遗忘方面面临独特挑战。现有方法要么需要计算上不可行的完全重训练，要么未能考虑用户行为的序列性质。我们提出了SURF（用于推荐遗忘的减法更新），一个用于SRS中近似机器遗忘的轻量级框架。SURF分三个阶段运行：（i）在嵌入空间中识别要遗忘物品的邻域，（ii）在这个紧凑的局部子集上训练一个辅助模型，以及（iii）在推理时从原始模型中减去辅助模型的分数。在7个数据集上与五个基线进行的实验表明，SURF实现了与完全重训练相当的有效遗忘，同时大幅降低了计算成本，在NDCG@20上取得了高达32%的提升，而所需时间仅为原始重训练基线时间预算的2%。我们在以下网址共享代码：此https URL。
英文摘要
The increasing demand for user privacy and compliance with regulations such as GDPR has made machine unlearning a fundamental requirement for modern recommender systems. However, Sequential Recommender Systems (SRS) pose unique challenges for unlearning due to their reliance on temporal interaction patterns. Existing approaches either require computationally prohibitive full retraining or fail to account for the sequential nature of user behavior. We propose SURF (Subtractive Updates for Recommender Forgetting), a lightweight framework for approximate machine unlearning in SRS. SURF operates in three stages: (i) identifying the neighborhood of the item to forget in the embedding space, (ii) training an auxiliary model on this compact local subset, and (iii) subtracting the auxiliary model's scores from the original model at inference time. Experiments against five baselines on 7 datasets show that SURF achieves unlearning effectiveness comparable to full retraining while substantially reducing computational cost, yielding up to a 32% improvement in NDCG@20 while requiring just 2% of the original retraining baseline time budget. We share our code at https://github.com/FilippoBetello/SURF.
发表机构
- Sapienza University of Rome（罗马大学）
- University of Pisa（比萨大学）
机构由 AI 辅助整理，请以论文原文为准。
