Benzi, KirellKalofolias, VassilisBresson, XavierVandergheynst, Pierre2016-01-082016-01-082016-01-08201610.1109/ICASSP.2016.7472115https://infoscience.epfl.ch/handle/20.500.14299/122094WOS:000388373402116This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using a rich combination of audio, meta-data and social features. As we demonstrate, our hybrid recommendation system is very versatile and incorporates several well-known methods while outperforming them. Particularly, we show on real-world data that our model overcomes w.r.t. two evaluation metrics the recommendation of models solely based on low-rank information, graph-based information or a combination of both.Recommender systemGraphsNMFTotal variationAudio featurestext::conference output::conference proceedings::conference paper