Having in mind today's growth of information sources, both in terms of their number and of their size, whether we are referring to the Internet, a corporate intranet, or a library information retrieval system, we can say that manipulating information is not a trivial task. The user is not often being catered for in distributed information systems. He/ she seems to be interacting with systems that do not recognize his/her uniqueness and thus do not offer an individualized treatment. As a result, User Modeling is a core, essential factor in achieving personalization. We present here an intelligent way of inferring user related information that is not available, a situation that is very likely to occur due to sparseness of relevant data. This method can be very useful in recommender systems and this is illustrated with an example.