Today, there are valuable sources of information concerning the personalized recommendation problem like user annotated internet sites, user interaction logs, online user communities. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of web usage data by integrating the user related information sources together. In this thesis, a method based on probabilistic latent semantic analysis is used as a framework for a hybrid social recommendation system. Different data hybridization approaches on probabilistic latent semantic analysis are experimented....