2016
Song recommendation with non-negative matrix factorization and graph total variation
ICASSP 2016accepted
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 son…