Next Check-in Location Prediction Combines with Collaborative Filtering

Abstract

With the thriving of mobile location-based services, location-based social networks (LBSNs) have become a popular form of social media recently. Large numbers of “check-in” records continue to accumulate over time, which contain rich information of social and geographical context and provide a unique opportunity for researchers to study user’s social behavior, which in turn enables a variety of services including place advertisement, intelligent transportation, and Urban Computing. In this paper, we mainly study the problem of user’s location prediction. Firstly we analyzed the regular mobility patterns about the visited locations. However, on account of personality trait of neophilia, people also show propensities of novelty seeking in human mobility, such as exploring unvisited but tailored locations for them to visit. Hence, we not only propose a set of features that aim to describe the regular mobility patterns, but also use the collaborative social knowledge to analysis the users’ mobility patterns on the unvisited locations. Finally, we further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, to get a higher overall prediction accuracy. And we find that the supervised methodology based on M5 model trees the offers better prediction accuracy.