Mobile Big-Data-Driven Rating Framework: Measuring the Relationship between Human Mobility and App Usage Behavior
Mobile Big-Data-Driven Rating Framework: Measuring the Relationship between Human Mobility and App Usage Behavior
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DOI：
10.1109/mnet.2016.7474339
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发表时间：
2016-05-01
期刊：
影响因子：
9.3
通讯作者：
Liu, Jiajia
中科院分区：
文献类型：
--
作者：
Qiao, Yuanyuan;Zhao, Xiaoxing;Liu, Jiajia
智能设备为我们带来了无处不在的移动的互联网接入，使在移动的环境中上网成为可能。随着移动的互联网的普及，大量证据表明，人类的移动性对应用程序的使用行为有很大的影响。然而，它们之间的关系尚未以任何形式量化。在这篇文章中，一个评级框架，以证明它们之间的联系的存在。评级框架的核心思想是选择可能影响应用使用行为的最重要的移动功能。特别是，我们专注于三个方面的人在城市地区的流动性：个人的流动性特征，位置和旅行行为，从人群和个人的角度来看。最后，通过使用有限数量的选定的移动性和时间特征，在人群和个人的应用程序使用行为方面实现了较高的预测精度，这验证了评级框架的有效性。
Smart devices bring us ubiquitous mobile access to the Internet, making it possible to surf the Internet in mobile environments. With the pervasiveness of mobile Internet, much evidence shows that human mobility has heavy impact on app usage behavior. However, the relationship between them has not been quantified in any form. In this article, a rating framework is presented to demonstrate the existence of their connection. The core idea of a rating framework selects the most significant mobility features that may influence app usage behavior. In particular, we focus on three aspects of human mobility in urban areas: individual mobility characteristics, location, and travel behavior, from both the crowd and individual points of view. At last, by using a limited number of selected mobility and time features, high forecast accuracy is achieved in terms of app usage behavior of crowds and individuals, which verifies the effectiveness of the rating framework.