Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed.