TY - JOUR
T1 - Mobile big-data-driven rating framework
T2 - Measuring the relationship between human mobility and app usage behavior
AU - Qiao, Yuanyuan
AU - Zhao, Xiaoxing
AU - Yang, Jie
AU - Liu, Jiajia
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2016/5/1
Y1 - 2016/5/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84971425443&partnerID=8YFLogxK
U2 - 10.1109/MNET.2016.7474339
DO - 10.1109/MNET.2016.7474339
M3 - 文章
AN - SCOPUS:84971425443
SN - 0890-8044
VL - 30
SP - 14
EP - 21
JO - IEEE Network
JF - IEEE Network
IS - 3
M1 - 7474339
ER -