Evaluation of Markov Models in Location Based Social Networks in Terms of Prediction Accuracy

Abstract: Location Based Social Networks has attracted millions of mobile internet users. On their smart phones people can share their locations using social network services. The main purpose of check-ins is to provide other users’ information about places they visit. Location Based Social Network with thousands of check-ins allows users to learn social behavior through spatial-temporal effect, which provides different services such as place recommendation and traffic prediction. Through this information, we can have an idea about important locations in the city and human mobility. The main purpose of this thesis is to evaluate Markov Models in Location Based Social Networks in terms of prediction accuracy. Location Based Social Network features and basic information’s will be analyzed before modeling of human mobility. Afterwards with the use of three methods human mobility will be modeled. In all the models the check-ins are analyzed based on prior category. After estimation the user’s possible next check-in category, and according to the user’s check-ins in the following category, it predicts the next possible check-in location. Finally a comparison will be made considering the models prediction accuracy.