In recent years, social networks have shown a great influx of new users and traffic. As their popularity grows, so does the interest in researching ways to process the information available, in order to produce useful knowledge. One direction is making personalized recommendations based on users’ preferences and on their social behavior and related characteristics in general. Static recommendations, however, are proven to be highly inaccurate, since as time progresses, people tend to change their preferences, making different decisions than the ones predicted previously. This calls for an adaptive algorithm that shifts according to the changes in preferences and habits of the users. Handling the stream of information is challenging, as the new data can severely change the recommendations to many users. In this work, we propose a novel streaming Point-of-Interest recommendation algorithm that explicitly incorporates location-aware features into its dynamic update mechanism, enabling continuous adaptation to newly arriving data. The proposed approach is experimentally evaluated based on real-life data sets containing the network structure as well as check-in information. The results demonstrate high accuracy, achieving at the same time significant performance gains with respect to runtime costs compared to conventional approaches.