Recommender systems provide relevant items to users from a large number of choices. In this work, we are interested in personalized recommender systems where user model is based on an analysis of usage. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Each technique has its drawbacks, so hybrid solutions, combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. In this paper, we propose a hybrid solution combining collaborative filtering and content-based filtering. With this aim, we have defined a new user model, called user semantic model, to model user semantic preferences based on items' features and user ratings. The user semantic model is built from the user-item model by using a fuzzy clustering algorithm: the Fuzzy C Mean (FCM) algorithm. Then, we used the user semantic model in a user-based collaborative filtering algorithm to calculate the similarity between users. Applying our approach to the MoviesLens dataset, significant improvements can be noticed comparatively to standards user-based and item-based collaborative filtering algorithms.