A mobility prediction model for location-based social networks

Abstract

Mobility prediction plays important roles in many fields. For example, tourist companies would like to know the characteristics of their customer movements so that they could design appropriate advertising strategies; sociologists has made many research on migration to try to find general features in human mobility; polices also analyze human movement behaviors to seek criminals. Thus, for location-based social networks, mobility prediction is an important task. This study proposes a mobility prediction model, which can be used to predict the user (human) mobility. The proposed approach is conducted from three characteristics: (1) regular movement in human mobility, (2) the influence of relationships on social networks, (3) other features (in this work, we consider “hot regions” where attract more people coming to there). To validate the proposed approach, three datasets including over 500,000 check-ins which are collected from two location-based social networks, namely Brightkite and Gowalla, are used for the experiments. Results show that the proposed model significantly improves the prediction accuracy, thus, this approach could be promising for mobility prediction, especially for location-based social networks.