In this thesis, a content recommendation system has been developed. The system makes recommendations based on the preferences of the users on some aspects of the content and also preferences of similar users. The preferences of a user are extracted from the choices of that user made in the past. Similarities between users are defined by the similarities of their preferences. Such a system requires both qualified content and user information. The proposed system uses semantic user and content profiles to more effectively define the relationships between the two and make better inferences. An ontology is defined using the existing domain ontologies and the semi-structured data on the web. The system is implemented mainly for the movie domain in which well-defined ontologies and user information are easier to access.