Song recommendation with non-negative matrix factorization and graph total variation

Kirell Benzi; Vassilis Kalofolias; Xavier Bresson; Pierre Vandergheynst

ICASSP 2016accepted0 citations

Song recommendation with non-negative matrix factorization and graph total variation

Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst

Abstract

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.

BibTeX

@inproceedings{icassp2016_songrecommendati,
  title = {Song recommendation with non-negative matrix factorization and graph total variation},
  author = {Kirell Benzi and Vassilis Kalofolias and Xavier Bresson and Pierre Vandergheynst},
  booktitle = {ICASSP 2016},
  year = {2016}
}