Intelligent Information Access by Learning WordNet-Based User Profiles

The central argument of this paper the induction user profiles by supervised machine learning techniques for Intelligent Information Access. The access must be highly personalized by user profiles, in which representations of the users’ interests are maintained. Moreover, users want to retrieve information on the basis of conceptual content, but individual words provide unreliable evidence about the content of documents. A possible solution is the adoption of WordNet as a lexical resource to induce semantic user profiles.