Abstract
The increasing use of social network resources has increased the users demands through these networks. Analyzing social media enables us to benefit from the rich source of multimedia in a personalized, adaptive and systematic way for building smart cities environments. This paper proposes an algorithm to fill the gap between the social media resources, user personalized preferences and the collaborative opinions. The algorithm utilizes social tagging to contribute in increasing the quality for searching for places, services, and the discovering communities of the same interest. By exploiting social tagging, we propose a model which analyzes comments and feedback of posts published by users online. We demonstrate how the proposed algorithm can better utilize social information to improve the discovering of relevant items for a given user. The experiment results show feasibility of filtering and matching items of a similar interest for individual users and a social community.