Omar F.Almallah and Songul Albayrak, Yildiz Technical University, Turkey

The circulation of the social networks and the evolution of the mobile phone devices has led to a big usage of location based social networks application such as Foursquare, Twitter, Swarm and Zomato on mobile phone devices mean that huge dataset which is containing a blend of information about users behaviour’s, social society network of each users and also information about each of venues, all these information available in mobile location recommendation system .These datasets are much more different from those which is used in online recommender systems, these datasets have more information and details about the users and the venues which is allowing to have more clear result with much more higher accuracy of the analysing in the result. In this paper we examine the users behaviour’s and the popularity of the venue through a large check-ins dataset from a location based social services, Foursquare: by using large scale dataset containing both user check-in and location information .Our analysis expose across 3 different cities.On analysis of these dataset reveal a different mobility habits, preferring places and also location patterns in the user personality. This information about the users behaviour’s and each of the location popularity can be used to know the recommendation systems and to predict the next move of the users depending on the categories that the users attend to visit and according to the history of each users check-ins.

Personalized Recommendations, Location based social networks.