Interdisciplinary Centre for Security, Reliability and Trust,

With the increasing availability of location-acquisition technolo-gies, we have better access to collections of large spatio-temporal datasets. This brings new opportunities to location-based services (LBS), especially when knowledge of users ’ movement behaviour (i.e., mobility profiles) can be extracted from such datasets. For in-stance, in social networks, friends can be recommended according to similarity scores between user mobility profiles. In this paper, we propose a new approach to construct users ’ mo-bility profiles and calculate the mobility similarities between users. We model mobility profiles as traces of places that users frequently visit and use frequent sequential pattern mining technologies to ex-tract them. To compare users ’ mobility profiles, we first discuss the weakness of a similarity measurement in the literature and then pro-pose our new measurement. We evaluate our work using a real-life dataset published by Microsoft Research Asia and the experimental results show that our approach outperforms the existing works on different aspects