Matrix factorization (MF) is a powerful approach used in recommender systems. One main drawback of MF is the dif- ficulty to interpret the automatically formed features. Fol- lowing the intuition that the relation between users and items can be expressed through a reduced set of users, re- ferred to as representative users, we propose a simple mod- ification of a traditional MF algorithm, that forms a set of features corresponding to these representative users. On one state of the art dataset, we show that the proposed representative users-based non-negative matrix factorization (RU-NMF) discovers interpretable features, while slightly (in some cases insignificantly) decreasing the accuracy.