• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems
【24h】

A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems

机译：一个隐私保留的分布式上下文联合联盟在线学习框架，具有在社交推荐系统中的大数据支持

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users' preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users' preferences according to the user context and users' history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom. We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.
机译：如今，大数据分析的蓬勃发展以及计算能力和网络带宽的约束使得独立代理/服务提供商难以为每个用户提供限时在有限时间内的每个用户提供适当的信息。为了处理这一挑战，推荐系统（RS）可以调用一组代理商来协作以学习用户的偏好和品味，称为分布式推荐系统（DRS）。 DRSS可以通过请求代理商彼此共享信息来提高传统RS的准确性。然而，由于候选人的数量，DRSS为每个用户提供个性化建议是挑战。此外，代理商之间的信息共享提出了隐私问题。因此，我们提出了一个隐私保留的DRS，然后将每个服务提供商塑造为具有上下文意识的分布式在线学习者。服务提供商通过根据用户上下文和用户的历史行为来学习用户的偏好来协作以使个性化建议进行个性化建议。我们采用联合学习框架来帮助培训高质量的隐私保留集中模型，这些模型可能与相对较慢的网络连接可能不可靠的分布式代理。为了处理大数据场景，我们构建一个项目群集树以将在线和将数据集从上到下处理。我们进一步考虑了社交网络的结构，并提出了一种有效的算法，以避免自适应性能损失。理论证据表明，我们的算法可以同时为服务提供商和用户同时实现Sublinear遗憾和差异隐私保护。数值结果证实，我们的小说框架可以处理越来越大的数据集并在隐私保留水平和预测准确性之间击打权衡。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号