Temporal Markov Chain Location Prediction

With the rapid development of social mobile networks, it is highly desirable to be able to accurately predict the next check-in location. Most methods of social mobile networks are mainly based on vast user check-in historic records to analyze the feature of users’ behaviors. The Markov Model is a frequently used method in location prediction problem. However, due to the diversity and sparsity of the social mobile network check-in data, tradition Markov Model location prediction, cannot take full advantages of potential information existed in plentiful check-in data. Then, we propose a prediction algorithm, temporal Markov Model (TMM) based on time characteristic. This algorithm relies on Markov Model to make a primary prediction of user checkin position. Then, utilizing the temporal character modifies the forecast result. We have conducted extensive experiments in two real datasets, and the results demonstrate the superiority of TMM over traditional Markov Models. Introduction With the rapid development of the Internet, mobile social network has become a widely used platform for user communications and sharing information. Moreover, the popularization of intelligent terminal and the generalization of GPS accelerate the fusion of social network and location based service. LBSN allows user sign in real world, it also support user to upload their check-in data and share with their friends. LBSN effectively reduces the distance between the virtual world and the real world, that’s why LBSN get the favors of many users. Using check-in data analyze hobby of users can provide convenience for user experiencing life and making friends, it also offers applications in intelligent transportation, urban computing, etc. Meanwhile, it has huge commercial value in terms of online advertising, mobile recommendation and other commercial applications. Investigations of the location data have more and more become the focus in the recent research, such as location naming, location recommendation etc. Location prediction research aimed to mine user check-in behavior through user’s historical record to predict the user's future position. At present, there are many classic location prediction methods, these models are mostly based on frequent patterns and association rules, and some research work has showed that frequency of human check-ins follows power-law distribution. A large number of check-in behaviors are concentrated on a certain locations [1, 2]. Some researchers use this characteristic. They use the frequency of users check-in at a location as an important indicator and propose MFC models to predict the next location that user will check-in. However, due to the diversity of mobile social network check-in information, relying solely on the frequency of check-in as the basis to predict is insufficient. The study of human movement patterns shows [1] that people's actions are repetitive. People repeat visits several fixed locations at a relatively fixed time every day. In real life, due to work, family and other reasons people have relatively fixed daily schedule, often at the same time to the same location to perform daily activities. In this paper, we firstly analyze the temporal characteristics of the users check-in behavior in LSBN, then proposed a location prediction algorithm based on temporal characteristic. The algorithm uses Markov model to predict the position of the modeling problem, and then use the user's check-in temporal characteristics to correct predicted results to get a better prediction.