SURF: Subtractive Updates for Recommender Forgetting

2026-09-16

AI Summary

为解决推荐系统中用户隐私保护及符合GDPR要求的问题，提出SURF框架，通过局部更新而非完全重训练的方法实现高效机器遗忘。

Abstract

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.

Problem

• machine unlearning
• Sequential Recommender Systems
• user privacy
• GDPR
• temporal interaction patterns

Innovation

• Subtractive Updates
• Recommender Forgetting
• Sequential Recommender Systems
• Machine Unlearning