Predicting mobile users’behaviors and locations using dynamic Bayesian networks

作     者：Jianrong Hou Hui Zhao Xiaofeng Zhao Jie Zhang 

作者机构：Antai College of Economics and ManagementShanghai Jiao Tong UniversityShanghaiChina School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina University of Mary WashingtonFredericksburgUSA 

出 版 物：《Journal of Management Analytics》 (管理分析学报（英文）)

年 卷 期：2016年第3卷第3期

页      面：191-205页

学科分类：0202[经济学-应用经济学] 1202[管理学-工商管理] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：The project is supported by the National Natural Science Foundation of China[grant number 71572109]. 

主　　题：location prediction dynamic Bayesian network majority voting social interaction Instagram 

摘      要：This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in different locations and online social interactions.We combine dynamic Bayesian networks with a majority voting model which is based on social interaction information to estimate the users’behaviors and predict the locations.By analyzing Instagram media records,spanning a period of 3 months,we explore rarely visited locations,which are often ignored as noise in previous research.In comparison,our model,using Instagram data with two existing location prediction models,shows that(1)our location prediction is more accurate and robust in both the general location and the location outside the living area;(2)social relations are instrumental in the location prediction as social interaction information can increase the accuracy of the prediction.