Collaborative filtering algorithms predict the preferences of a user for an item by weighting the contributions of similar users, called neighbors, for that item. Similarity between users is computed by comparing their rating styles, i.e. the set of ratings given on the same items. Unfortunately, similarity between users is computable only if they have common rated items. The main contribution of this paper is a (content-collaborative) hybrid recommender system which overcomes this limitation by computing similarity between users on the ground of their content-based profiles. Traditional keyword-based profiles are unable to capture the semantics of user interests, due to the natural language ambiguity. A distinctive feature of the proposed technique is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in the WordNet lexical database. This model, called the semantic user profile, is exploited by the hybrid recommender in the neighborhood formation process. The results of an experimental session in a movie recommendation scenario demonstrate the effectiveness of the proposed approach.
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