• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on User Modeling(UM 2007); 20070625-29; Corfu(GR) >Learning from What Others Know: Privacy Preserving Cross System Personalization
【24h】

Learning from What Others Know: Privacy Preserving Cross System Personalization

机译：向其他人学习：隐私保护跨系统个性化

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

Recommender systems have been steadily gaining popularity and have been deployed by several service providers. Large scalable deployment has however highlighted one of the design problems of recommender systems: lack of interoperability. Users today often use multiple electronic systems offering recommendations, which cannot learn from one another. The result is that the end user has to often provide similar information and in some cases disjoint information. Intuitively, it seems that much can be improved with this situation: information learnt by one system could potentially be reused by another, to offer an overall improved personalization experience. In this paper, we provide an effective solution to this problem using Latent Semantic Models by learning a user model across multiple systems. A privacy preserving distributed framework is added around the traditional Probabilistic Latent Semantic Analysis framework, and practical aspects such as addition of new systems and items are also dealt with in this work.
机译：推荐系统已稳步普及，并已被多家服务提供商部署。但是，大规模的可扩展部署突出了推荐系统的设计问题之一：缺乏互操作性。如今，用户经常使用多个提供建议的电子系统，这些系统无法相互学习。结果是最终用户必须经常提供类似的信息，并且在某些情况下不相交的信息。直观上，这种情况似乎可以改善很多：一个系统学习到的信息可能会被另一个系统重用，以提供整体上改进的个性化体验。在本文中，我们通过学习跨多个系统的用户模型，使用潜在语义模型为该问题提供了有效的解决方案。围绕传统的概率潜在语义分析框架添加了一个保护隐私的分布式框架，并且在此工作中还处理了诸如添加新系统和项目之类的实际方面。

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