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《Journal of Geo-Information Science》 2017-08
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A mixed Markov Method to Predict the Surfing Time Period of Mobile Phone Users

FANG Zhixiang;YU Chong;ZHANG Tao;FENG Mingxiang;NI Yaqian;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University;Collaborative Innovation Center of Geospatial Technology;Business Support Center,Hubei Mobile;  
In recent years, big data of mobile phones has become a great data source for researches and applications. It has been widely used to understand the human behaviors in cyberspace space. Researching and forecasting the surfing time of mobile phone users have great significance for analyzing mobile phone users' behaviors and patterns, designing network service, and understanding the relationship of surfing behaviors, website stickiness, users' psychology, mobile Internet intelligent business. We proposed a mixed Markov method(Lift-Markov method. LM), combining the traditional Markov model and association rule model, to predict the surfing time period of mobile phone users. A dataset of surfing records of 4G mobile phone users collected by Hubei Mobile within twenty days is used to demonstrate the capability of predicting web-surfing time periods of users. LM method has a better prediction accuracy when it is compared with the traditional Markov model and the Most-value model. There are two main findings here: the first one is that there is obvious periodicity in surfing time periods of 37.66% mobile phone users in experimental area by Fourier transformation and periodic tests, which could help us understand the surfing characteristics of users. Also, the second one is that the average accuracy of our proposed method is better than the Markov model and the Most-value model in 10 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes and 60 minutes intervals. LM method can perform an average accuracy of79.75% in predicting web-surfing time on a scale of 60 minutes, better than the Markov model(74.64%) and the Most-value model(64.44%). Compared with the other two models, the accuracy distribution of the LM method is narrower, the peak value is higher, and the standard deviation is smaller, which means that the prediction accuracy of the LM method is more concentrated and stable, with good predictive performance.
【Fund】: 国家自然科学基金项目(41231171、41371420);; 湖北省青年英才开发计划;; 武汉大学自主科研项目拔尖创新人才类资助项目(2042015KF0167)
【CateGory Index】: O211.62;TN929.53
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