  Structured sparse coding and the related structured dictionary learning
problems are novel research areas in machine learning. In this paper we present
a new application of structured dictionary learning for collaborative filtering
based recommender systems. Our extensive numerical experiments demonstrate that
the presented technique outperforms its state-of-the-art competitors and has
several advantages over approaches that do not put structured constraints on
the dictionary elements.
