Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data

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.