Finding high-quality sources in the expanding micro-blogging community using Twitter becomes essential for information seekers in order to cope with information overload. In this paper, we present a recommendation algorithm aiming to identify potentially interesting users to follow in the Twitter network. This algorithm first explores the graph of connections starting at the target user (the user to whom we wish to recommend previously unknown followees) in order to select a set of candidate users to recommend, according to an heuristic procedure. The set of candidate users is then ranked according to the similarity between the content of tweets that they publish and the target user interests. Experimental evaluation was conducted to determine the impact of different profiling strategies.