Toward ontology-based personalization of a recommender system in social network

Personalized search, navigation and content delivery techniques have attracted interest in the recommender systems as a means to decrease search ambiguity and return results most relevant to a particular user preferences. In this paper, we study the effect of incorporating user semantic profile derived from past user's behavior and preferences on the accuracy of a recommender system. We present a preliminary work which aims at tackling the most technical issues due to the integration of an ontology-based semantic user profile within a hybrid recommender system based on our early released guided recommender algorithm. A semantic user profile context is represented as an instance of a reference domain ontology in which concepts are annotated by interest scores.