Paper Info
Title
Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data.
Abstract
With the rapid development of wireless communication and mobile Internet, mobile phone becomes ubiquitous and functions as a versatile and a smart system, on which people frequently interact with various mobile applications (Apps). Understanding human behaviors using mobile phone is significant for mobile system developers, for human-centered system optimization and better service provisioning. In this paper, we focus on mobile user behavior analysis and prediction based on mobile Internet traffic data. Real traffic flow data is collected from the public network of Internet service providers, by high-performance network traffic monitors. We construct a User-App bipartite network to represent the traffic interaction pattern between users and App servers. After mining the explicit and implicit features from the User-App bipartite network, we propose two positive and unlabeled (PU) learning methods, including Spy-based PU learning and K-means-based PU learning, for App usage prediction and mobile video traffic identification. We first use the traffic flow data of QQ, a very famous messaging and social media application possessing high market share in China, as the experimental data set for App usage prediction task. Then, we use the traffic flow data from six popular Apps, including video intensive Apps (Youku, Baofeng, LeTV, and Tudou) and other Apps (Meituan and Apple), as the experimental data set for mobile video traffic identification task. Experimental results show that our proposed PU learning methods perform well in both tasks.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2852008
IEEE ACCESS
Keywords
Field
DocType
Mobile user behavior,PU learning,App usage prediction,mobile video traffic identification,bipartite network
Wireless,Traffic flow,Smart system,Task analysis,Computer science,Server,Computer network,Human behavior,Mobile phone,The Internet
Journal
Volume
ISSN
Citations 
6
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ke Yu115218.85
Yue Liu244184.32
Linbo Qing33814.63
Binbin Wang432.76
Yongqiang Cheng545.44