Paper Info
Title
Characterizing a user from large-scale smartphone-sensed data.
Abstract
Device analyzer can provide a large-scale dataset that captures real-world usage of smart phones [1]. Detailed usage records in smart phones, conveying a partial life log, are important for a deep scientific understanding of human characteristics. In this study, we proposed a feature-based labeling method to characterize users. Eight features from three aspects, i.e., daily mobility, user daily schedule, and social ability, are designed within a time window. Further, we analyze the features' correlation and variation over time. With the features, each user can be attached with a few semantic labels to demonstrate his/her characteristics. This work is a promising step towards drawing "portraits" for users using mobile phone data.
Year
DOI
Venue
2017
10.1145/3123024.3124437
UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing Maui Hawaii September, 2017
Field
DocType
ISBN
Mobile sensing,Clock synchronisation,Computer science,Mobile device,Mobile phone,Spectrum analyzer,Embedded system
Conference
978-1-4503-5190-4
Citations 
PageRank 
References 
1
0.35
5
Authors
7
Name
Order
Citations
PageRank
Sha Zhao1489.96
Y. Zhao227733.44
Zhe Zhao330.76
Zhiling Luo4388.77
Runhe Huang540756.46
Shijian Li6115569.34
Gang Pan71501123.57