Recommender Systems represent useful tools helping users to find “what they need” from a very large number of candidates and supporting people in making decisions in several contexts. In this paper, we propose a novel user-centered and social recommendation approach in which several aspects related to users, i.e., preferences, opinions, behavior, feedbacks, are considered and integrated together with items’ features and context information within a general framework that can support different applications using proper customizations (e.g., recommendation of news, photos, movies, travels, etc.). Preliminary experiments on system accuracy show how our approach provides very promising and interesting results.