论文标题
使推荐系统忘记：学习和学习以擦除建议
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
论文作者
论文摘要
隐私法律和法规强制执行数据驱动的系统，例如推荐系统，以删除有关个人的数据。随着机器学习模型可能会记住培训数据，数据擦除也应在模型中删除数据谱系，从而增加了对机器未学习问题（MU）的兴趣。但是，现有的MU方法不能直接应用于建议中。大多数推荐系统的基本思想是协作过滤，但是现有的MU方法忽略了用户和项目之间的协作信息。在本文中，我们提出了一个一般可擦除的建议框架，即激光器，该框架由组模块和SEQTRAIN模块组成。首先，组模块根据通过HyperGraph学习的协作嵌入的相似性将用户分为平衡的组。然后，Seqtrain模块在所有学习课程学习的组上依次训练该模型。在两个现实世界数据集上的理论分析和实验都表明，激光不仅可以实现有效的未学习，而且还可以优于最先进的学习框架，从模型效用方面。
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the training data, data erasure should also unlearn the data lineage in models, which raises increasing interest in the problem of Machine Unlearning (MU). However, existing MU methods cannot be directly applied into recommendation. The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items. In this paper, we propose a general erasable recommendation framework, namely LASER, which consists of Group module and SeqTrain module. Firstly, Group module partitions users into balanced groups based on their similarity of collaborative embedding learned via hypergraph. Then SeqTrain module trains the model sequentially on all groups with curriculum learning. Both theoretical analysis and experiments on two real-world datasets demonstrate that LASER can not only achieve efficient unlearning, but also outperform the state-of-the-art unlearning framework in terms of model utility.
