Predicting mobile users’ behaviors and locations using dynamic Bayesian networks

Abstract

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.

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