Matrix factorization (MF) is one of the most powerful ap- proaches used in the frame of recommender systems. It aims to model the preferences of users about items through a reduced set of latent features. One main drawback of MF is the difficulty to interpret the automatically formed features. Following the intuition that the relation between users and items can be expressed through a reduced set of users, referred to as representative users, we propose a simple modification of a traditional MF algorithm, that forms a set of features corresponding to these repre- sentative users. On one state of the art dataset, we show that proposed representative users-based non-negative matrix factorization (RU-NMF) discovers interpretable features and does not significantly decrease the accuracy on test with 10 and 15 features.