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首页> 外文会议>International Conference on Cloud Computing and Security >DLPDS: Learning Users' Information Sharing Behaviors for Privacy Default Setting in Recommender System
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DLPDS: Learning Users' Information Sharing Behaviors for Privacy Default Setting in Recommender System

机译：DLPDS：学习用户的信息共享行为，以推荐系统中的隐私默认设置

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The proliferation of Internet of things has allowed users to provide preference feedback and maintain profiles in multiple websites, which could indicate tastes covering several kinds of domains but focus on small number of topics. Leveraging all the user information available in several sites or domains may be beneficial for knowing the users better and generating higher-quality recommendations. However, aggregating all users' information globally could trigger users' awareness on privacy concerns, which further cause users refuse to share the information so that the recommendation quality is reduced. We provide evidence that a recommender system could mitigate the privacy-integration problem, when it is applied with our novel model, called disclosure-learning privacy default setting (DLPDS), which transfer the pattern of users' past information sharing behaviors into privacy default settings. The result of the experiment support that our DLPDS model could gain users' trust and aggregate more users information, and that adapting the privacy default settings to the user information sharing pattern may results in positive feedback that promoting better prediction accuracy of the recommender system.
机译：物联网的普及使用户可以提供偏好反馈并在多个网站上维护个人资料，这可能表明口味涵盖了多种领域，但只关注少数主题。利用多个站点或域中可用的所有用户信息，可能有助于更好地了解用户并生成更高质量的建议。但是，在全球范围内汇总所有用户的信息可能会触发用户对隐私问题的意识，这进一步导致用户拒​​绝共享信息，从而降低了推荐质量。我们提供的证据表明，当推荐系统与我们称为公开学习隐私默认设置（DLPDS）的新型模型一起应用时，推荐系统可以缓解隐私集成问题，该模型将用户过去的信息共享行为模式转换为隐私默认设置。实验结果表明，我们的DLPDS模型可以赢得用户的信任并聚集更多的用户信息，并且将隐私默认设置适应用户信息共享模式可以产生积极的反馈，从而促进推荐系统的更好的预测准确性。

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