Abstract : This thesis is devoted to identify the profile of hypermedia user, then to adapt it according to user's profile. This profile is found by using supervised learning algorithm like SVM. The user model is one of the essential components of adaptive hypermedia. One way to characterize this model is to associate a user to a profile. Web Usage Mining (WUM) identifies this profile from traces. However, these techniques usually operate on large mass of data. In the case when not enough data are available, we propose to use the structure and the content of the hypermedia. Hence, we used supervised kernel learning algorithms for which we have defined the measure of similarity between traces based on a "distance" between documents of the site. Our approach was validated using synthetic data and then using real data from the traces of Hypergéo users, Hypergéo is an encyclopedic website specialized in geography. Our results were compared with those obtained using a techniques of WUM(the algorithm of characteristic patterns). Finally, our proposals to identify the profiles a posteriori led usto highlight five profiles. Hypergéo users are classified according to their interests when the "semantic distance" between documents is applied.