论文标题
一个联合的多视图深度学习框架，用于隐私推荐建议
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
论文作者
论文摘要
由于对用户隐私和数据安全性的严重担忧，因此，由于分散的用户数据越来越难以通过建议服务提供商收集，因此保护隐私的建议最近正在获得势头。严格的政府法规，例如欧洲的一般数据隐私法规（GDPR），这种情况进一步加剧了这种情况。联合学习（FL）是一种新开发的保护隐私机器学习范式，可以在不损害数据安全和隐私的情况下桥接数据存储库。因此，已经提出了许多联合建议（FedRec）算法来实现个性化的隐私建议。但是，现有的FedRec算法（主要是从传统的协作过滤方法（CF）方法）扩展的，无法很好地解决冷启动问题。此外，他们的性能开销W.R.T.与集中建议相比，在联合环境中受过训练的模型准确性通常不可忽略。本文研究了此问题并提出了FL-MV-DSSM，这是一个基于内容的联合多视图推荐框架，不仅可以解决冷启动问题，而且通过从多个数据源中学习联合模型来捕获富裕用户级特征，从而显着提高了建议性能。 FL-MV-DSSM提出的新的联合多视图设置开设了新的用法模型，并在推荐方案中带来了新的安全挑战。我们证明了\ xxx的安全保证，以及对FL-MV-DSSM及其对公共数据集的变化的经验评估证明了其有效性。如果接受本文，我们的代码将发布。
Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to the serious concerns over user privacy and data security. This situation is further exacerbated by the strict government regulations such as Europe's General Data Privacy Regulations(GDPR). Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy. Thus many federated recommendation(FedRec) algorithms have been proposed to realize personalized privacy-preserving recommendations. However, existing FedRec algorithms, mostly extended from traditional collaborative filtering(CF) method, cannot address cold-start problem well. In addition, their performance overhead w.r.t. model accuracy, trained in a federated setting, is often non-negligible comparing to centralized recommendations. This paper studies this issue and presents FL-MV-DSSM, a generic content-based federated multi-view recommendation framework that not only addresses the cold-start problem, but also significantly boosts the recommendation performance by learning a federated model from multiple data source for capturing richer user-level features. The new federated multi-view setting, proposed by FL-MV-DSSM, opens new usage models and brings in new security challenges to FL in recommendation scenarios. We prove the security guarantees of \xxx, and empirical evaluations on FL-MV-DSSM and its variations with public datasets demonstrate its effectiveness. Our codes will be released if this paper is accepted.
