论文标题
一个基于联合学习的新型隐私保留推荐系统框架
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
论文作者
论文摘要
推荐系统（RS）当前是解决信息过载的有效方法。为了满足用户的下一次点击行为，RS需要收集用户的个人信息和行为，以实现全面而深刻的用户偏好感知。但是，这些集中收集的数据对隐私敏感，任何泄漏都可能给用户和服务提供商带来严重的问题。本文提出了一种新颖的隐私推荐系统框架（PPRSF），通过应用联合学习范式，以使建议算法能够接受培训并进行推理，而无需集中收集用户的私人数据。 PPRSF不仅能够降低隐私泄漏风险，满足法律和监管要求，而且还可以应用各种建议算法。
Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out inference without centrally collecting users' private data. The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied.
