Most recommender systems usually have too many items to recommend to too many users using limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This article outlines a collaborative recommender system, that tries to amend this situation. The system is built around the notion of k-separability combined with a constructive neural network algorithm.