提出快速去排名方法，高效消除推荐系统中敏感数据影响
Learning to Fast Unrank in Collaborative Filtering Recommendation
- 基于交互传播识别影响范围，分三步实现快速去排名
- 在多个数据集上比现有方法快50倍，效果接近重新训练
- 适合需要实时删除用户数据的推荐系统场景
现代数据驱动的推荐系统存在记忆敏感用户行为模式的风险，引发隐私担忧。现有推荐去学习方法虽能消除目标数据影响，但存在效率低、性能下降的问题，难以满足实时去学习需求。考虑到推荐系统以排序为导向的特点，我们提出“去排名”——即降低目标物品的排序位置，同时保证推荐去学习的形式化保障。为实现高效去排名，我们提出协同过滤推荐中的快速去排名学习方法（L2UnRank），包含三个关键阶段：(a) 通过基于交互的p跳传播识别影响范围；(b) 计算该范围内实体的结构与语义影响；(c) 基于影响信息进行高效、排序感知的参数更新。在多个数据集和主干模型上的实验表明，L2UnRank具有模型无关性，实现了当前最佳的去排名效果，并保持与重新训练相当的推荐质量，同时相比现有方法提速50倍。代码已公开于 https://github.com/Juniper42/L2UnRank。
原文摘要 · Abstract (English)
Modern data-driven recommendation systems risk memorizing sensitive user behavioral patterns, raising privacy concerns. Existing recommendation unlearning methods, while capable of removing target data influence, suffer from inefficient unlearning speed and degraded performance, failing to meet real-time unlearning demands. Considering the ranking-oriented nature of recommendation systems, we present unranking, the process of reducing the ranking positions of target items while ensuring the formal guarantees of recommendation unlearning. To achieve efficient unranking, we propose Learning to Fast Unrank in Collaborative Filtering Recommendation (L2UnRank), which operates through three key stages: (a) identifying the influenced scope via interaction-based p-hop propagation, (b) computing structural and semantic influences for entities within this scope, and (c) performing efficient, ranking-aware parameter updates guided by influence information. Extensive experiments across multiple datasets and backbone models demonstrate L2UnRank's model-agnostic nature, achieving state-of-the-art unranking effectiveness and maintaining recommendation quality comparable to retraining, while also delivering a 50x speedup over existing methods. Codes are available at https://github.com/Juniper42/L2UnRank.
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