Prediction of User’s Moving Location Based on Period-Near Algorithm

doi:10.3969/j.issn.1006-2475.2017.06.003

Abstract

Abstract: Sina Weibo is an electronic medium that allows a large number of users to share personal information with each other including location information, which makes it possible to know users’ movement. Even though users’ movement and mobility patterns have a high degree of freedom and variation, periodicity is a frequently happening phenomenon for users. Finding periodic behaviors is essential for understanding user movements. In this paper, we address the problem as to predict where a user will go. It involves two sub-problems: how to detect users’ historical behavior, and how to use historical behaviors to predict the behavior in the future. Our main assumptions are that users’ behaviors are periodic and a user will stay in one location if he or she stays in this location for a long time. Based on these assumptions, we propose a 4-stage algorithm, Period-Near, to solve the problem. At the first stage, we mine the periodic behaviors of a user, then, find frequent transfers. At the third stage, we aim to know where the user is in the nearest time. At last, we consider the three stages together to predict where the user will go in the next time. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of our method.

Key words: behavior prediction, frequent movement, real-life needs, cross location

CLC Number:

TP391

GAO Xia. Prediction of User’s Moving Location Based on Period-Near Algorithm[J]. Computer and Modernization, 2017, 0(6): 15-19.

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