The main aim of the recommendation systems is to deliver customized (personalized) information to a very differentiated users. They may be applied in a great variety of domains, such as: net-news filtering, web recommender, personalized newspaper, sharing news, movie recommender, document recommender, information recommender, e-commerce, purchase, travel and store recommender, e-mail filtering, music recommender, student courses recommender, user interface recommendation, negotiation systems, etc.. We consider two dimensions of the recommendation systems, user modeling and user model exploitation. The former considers user profile representation&maintenance and profile learning techniques. The later contains information filtering method, matching techniques and profile adaptation technique.