Positive and unlabeled learning for user behavior analysis based on mobile internet traffic data
Yu, Ke; Liu, Yue; Qing, Linbo; Wang, Binbin; Cheng, Yongqiang
Dr Yongqiang Cheng Y.Cheng@hull.ac.uk
With the rapid development of wireless communication and mobile Internet, mobile phone becomes ubiquitous and functions as a versatile and 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 (ISPs), by high-performance network traffic monitors.We construct User-App bipartite network to represent the traffic interaction pattern between users and App servers. After mining the explicit and implicit features from User-App bipartite network, we propose two positive and unlabeled learning (PU learning) methods, including Spy-based PU learning and K-means-based PU learning, for App usage prediction and mobile video traffic identification. We firstly use the traffic flow data of QQ, a very famous messaging and social media application possessing high market share in China, as the experimental dataset for App usage prediction task. Then we use the traffic flow data from six popular Apps, including video intensive Apps (Youku, Baofeng, LeTV, Tudou) and other Apps (Meituan, Apple), as the experimental dataset for mobile video traffic identification task. Experimental results show that our proposed PU learning methods perform well in both tasks.
|Journal Article Type||Article|
|Publication Date||Jun 29, 2018|
|Journal||IEEE access : practical innovations, open solutions|
|Publisher||Institute of Electrical and Electronics Engineers|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Yu, K., Liu, Y., Qing, L., Wang, B., & Cheng, Y. (2018). Positive and unlabeled learning for user behavior analysis based on mobile internet traffic data. IEEE access : practical innovations, open solutions, 6, 37568-37580. https://doi.org/10.1109/ACCESS.2018.2852008|
|Keywords||General Engineering; General Materials Science; General Computer Science|
|Copyright Statement||© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/pub...tions/rights/index.html for more information.
© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
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