Zhao Sha, Pan Gang, Tao Jianrong, Luo Zhiling, Li Shijian, Wu Zhaohui
IEEE Trans Cybern. 2022 Jan;52(1):384-397. doi: 10.1109/TCYB.2020.2967644. Epub 2022 Jan 11.
Smartphones are changing humans' lifestyles. Mobile applications (apps) on smartphones serve as entries for users to access a wide range of services in our daily lives. The apps installed on one's smartphone convey lots of personal information, such as demographics, interests, and needs. This provides a new lens to understand smartphone users. However, it is difficult to compactly characterize a user with his/her installed app list. In this article, a user representation framework is proposed, where we model the underlying relations between apps and users with Boolean matrix factorization (BMF). It builds a compact user subspace by discovering basic components from installed app lists. Each basic component encapsulates a semantic interpretation of a series of special-purpose apps, which is a reflection of user needs and interests. Each user is represented by a linear combination of the semantic basic components. With this user representation framework, we use supervised and unsupervised learning methods to understand users, including mining user attributes, discovering user groups, and labeling semantic tags to users. Extensive experiments were conducted on three data subsets from a large-scale real-world dataset for evaluation, each consisting of installed app lists from over 10 000 users. The results demonstrated the effectiveness of our user representation framework.
智能手机正在改变人类的生活方式。智能手机上的移动应用程序（应用）是用户在日常生活中访问各种服务的入口。安装在个人智能手机上的应用传达了大量个人信息，如人口统计学信息、兴趣和需求。这为理解智能手机用户提供了一个新视角。然而，仅通过用户安装的应用列表来简洁地描述用户特征是困难的。在本文中，我们提出了一个用户表示框架，在该框架中，我们使用布尔矩阵分解（BMF）对应用和用户之间的潜在关系进行建模。它通过从安装的应用列表中发现基本组件来构建一个紧凑的用户子空间。每个基本组件封装了一系列专用应用的语义解释，这反映了用户的需求和兴趣。每个用户由语义基本组件的线性组合表示。借助这个用户表示框架，我们使用监督学习和无监督学习方法来理解用户，包括挖掘用户属性、发现用户群体以及为用户标记语义标签。我们在一个大规模真实世界数据集的三个数据子集上进行了广泛实验以进行评估，每个子集都包含来自一万多名用户的安装应用列表。结果证明了我们的用户表示框架的有效性。
