  This 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.
