This paper describes the general design and architecture of an intelligent recommendation system aimed mainly at supporting a user in her navigation through the massive amounts of information that she has to cope with in order to find the right information. Alternative recommender system techniques are needed to retrieve quickly high quality recommendations even from a huge amount of data. Singular Value Decomposition-Collaborative Filtering (SVD-CF) methods are the techniques that are used in order to solve some recommender system problems by reducing the dimensionality of the product space, therefore producing better recommendations. Thanks to these techniques we can capture important latent associations between users and items. Also, users can benefit from the extension of their recommendation lists by taking into consideration the purchase of products that tend to be bought together.