Nowadays, the large availability of smart city data allows the analysis of users’ preferences and behaviors inside a town. The opportunity of providing a framework about the frequency of user check-ins in some places of a city might have a key role in maintenance, cleaning, security, and economic growth. This thesis work aims at exploiting a machine learning based approach to extract useful patterns from mobility data and location-based social network data. The proposed methodology addresses two main issues. The first one is related to suggest locations that users may appreciate, while the second regards an estimation of users’ displacements. The first problem implies the adoption of a clustering algorithm and a matrix factorization algorithm.