What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users?

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