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Constructing and Comparing User Mobility Profiles

Nowadays, the accumulation of people's whereabouts due to location-based applications has made it possible to construct their mobility profiles. This access to users' mobility profiles subsequently brings benefits back to location-based applications. For instance, in on-line social networks, friends can be recommended not only based on the similarity between their registered information, for instance, hobbies and professions but also referring to the similarity between their mobility profiles.
 In this article, we propose a new approach to construct and compare users' mobility profiles. First, we improve and apply frequent sequential pattern mining technologies to extract the sequences of places that a user frequently visits and use them to model his mobility profile. Second, we present a new method to calculate the similarity between two users using their mobility profiles. More specifically, we identify the weaknesses of a similarity metric in the literature, and propose a new one which not only fixes the weaknesses but also provides more precise and effective similarity estimation. Third, we consider the semantics of spatio-temporal information contained in user mobility profiles and add them into the calculation of user similarity. It enables us to measure users' similarity from different perspectives. Two specific types of semantics are explored in this article: location semantics and temporal semantics. Last, we validate our approach by applying it to two real-life datasets collected by Microsoft Research Asia and Yonsei University, respectively. The results show that our approach outperforms the existing works from several aspects.