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The increasing demand to access social networks by mobile devices together with increasing computation power of these mobile devices motivate the need of local recommendation services for social network users. Social networking is generating an incredible amount of information that is sometimes difficult for users to process, especially from mobile phones. Several links, activities, and recommendations are proposed by networked friends every hour, which together are nearly impossible to manage. There is a need to filter and make accessible such information to users, which is the motivation behind developing a mobile recommender that exploits social network information. Thus, in this paper, we propose the design and the implementation of a SOcial Mobile Activity Recommender (SOMAR) that can integrate Facebook social network mobile data and sensor data to propose activities to the user. The recommendations are completely calculated in situ in the mobile device with an embedded data mining component that is the basis to compute a social graph of the user relationships that will be later used for the recommendation process. The paper also presents some experiments that analyze the performance of the proposed method.