期刊
IEEE SYSTEMS JOURNAL
卷 11, 期 1, 页码 315-323出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2015.2431323
关键词
APP lists; mobile sensing; smartphones; user; attributes; user mining
类别
资金
- Program for New Century Excellent Talents in University [NCET-13-0521]
- National Key Basic Research Program of China [2013CB329504]
- National Key Technology RD Program [2012BAH94F03]
Prevalence of smartphones is changing people's lifestyle. Mobile applications (abbr. APPs) on a smartphone serve as entries for users to access a wide range of services. What APPs installed on one's smartphone, i.e., APP list, convey lots of information regarding his/her personal attributes, such as gender, occupation, income, and preferences. This paper addresses the discovery of user attributes from an APP list. We develop an attribute-specific representation to describe user characteristics and then model the relationship between an attribute and an APP list. A large-scale real-world data set with APP lists of more than 100 000 smartphones is used for evaluation. Our approach achieves the average equal error rate of 16.4% for 12 predefined user attributes. To our best knowledge, this is the first work to explore mining of user attributes from installed APP lists.
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