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Predicting User Traits From a Snapshot of Apps Installed on a Smartphone

机译:从智能手机上安装的应用程序快照预测用户特征

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摘要

Third party apps are an integral component of the smartphone ecosystem. In this paper, we investigate how user traits can be inferred by observing only a single snapshot of installed apps. Using supervised learning methods and minimal external information we show that user traits such as religion, relationship status, spoken languages, countries of interest, and whether or not the user is a parent of small children, can be easily predicted. Using data collected from over 200 smartphone users, specifically the list of installed apps and the corresponding ground truth traits of the users, we show that for most traits we can achieve over 90% precision. Our inference method can be used to provide services such as personalized content delivery or recommender systems for users. We also highlight privacy loss that can occur from unrestricted access to the app lists in popular smartphone operating systems.
机译:第三方应用程序是智能手机生态系统的组成部分。在本文中,我们研究了如何仅通过观察已安装应用程序的单个快照来推断用户特征。使用监督学习方法和最少的外部信息,我们可以轻松预测用户的特征,例如宗教,关系状况,口语,所关注的国家以及用户是否为小孩的父母。使用从200多个智能手机用户那里收集的数据,特别是已安装的应用程序列表以及用户相应的地面真实特征,我们表明,对于大多数特征,我们可以达到90%以上的精度。我们的推断方法可用于为用户提供服务,例如个性化内容交付或推荐系统。我们还着重强调了在流行的智能手机操作系统中不受限制地访问应用程序列表可能导致的隐私丢失。

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