Author/Authors :
Chen, Yong Big Data Technology and System Lab - Services Computing Technology and System Lab - Cluster and Grid Computing Lab, School of Computer Science and Technology - Huazhong University of Science and Technology, China , Yao, Juncheng Big Data Technology and System Lab - Services Computing Technology and System Lab - Cluster and Grid Computing Lab, School of Computer Science and Technology - Huazhong University of Science and Technology, China , He, Chunjiang China Electric Power Research Institute, China , Jin, Hai Big Data Technology and System Lab - Services Computing Technology and System Lab - Cluster and Grid Computing Lab, School of Computer Science and Technology - Huazhong University of Science and Technology, China , Chen, Hanhua Big Data Technology and System Lab - Services Computing Technology and System Lab - Cluster and Grid Computing Lab, School of Computer Science and Technology - Huazhong University of Science and Technology, China
Abstract :
The emergence and widespread use of mobile Internet technology has led to many different kinds of new mobile communications services, such as WeChat. Users could have more choices when attempting to satisfy their communications needs. The ability to predict the way in which users will use new mobile communications services is extremely valuable to mobile communications service providers. In this work, we propose a method for predicting how a user will use a new mobile service. Our scheme is inspired by the evolutionary game theory. With large-scale real world datasets collected from mobile service providers, we first extract the benefit-related features for users who were starting to use a new mobile service. Then we design our training and prediction methods for predicting potential users. We evaluate our scheme using experiments with large-scale real data. The results show that our approach can predict users’ future behavior with satisfying accuracy.
