Building recommender systems, that filters and suggests relevant and personalized information to its user, has become a practical challenge for researchers in AI. On the other hand, the relevance of such information is a user-dependent notion, defined within the scope or context of a particular domain or topic. Previous work, mainly in IR, focuses on the analysis of the content by means of keyword-based metrics. Some recent algorithms apply social or collaborative information filtering to improve the task of retrieving relevant information, and for refining each agent’s particular knowledge. In this paper, we combine both approaches developing a new content-based filtering technique for learning up-to-date users’ profile that serves as basis for a novel collaborative information-filtering algorithm. We demonstrate our approach through a system called RAAP (Research Assistant Agent Project) implemented to support collaborative research by classifying domain specific information, retrieved from the Web, and recommending these bookmarks to other researcher with similar research interests.