Mining user behaviours: a study of check-in patterns in location based social networks,” Web Science

Understanding the patterns underlying human mobility is of an essential importance to applications like recommender systems. In this paper we investigate the behaviour of around 10,000 frequent users of Location Based Social Networks (LBSNs) making use of their full movement patterns. We analyse the metadata associated with the whereabouts of the users, with emphasis on the type of places and their evolution over time. We uncover patterns across different temporal scales for venue category usage. Then, focusing on individual users, we apply this knowledge in two tasks: 1) clustering users based on their behaviour and 2) predict-ing users ’ future movements. By this, we demonstrate both qualitatively and quantitatively that incorporating tempo-ral regularities is beneficial for making better sense of user behaviour