Song recommendation with non-negative matrix factorization and graph total variation
By Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst. 2016
53 citations recorded by the source corpus.
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.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Federico Monti
- Michael M. Bronstein
- Xavier Bresson
- Vassilis Kalofolias
- Nathanael Perraudin
- Nauman Shahid
- Gilles Puy
- Vijay Prakash Dwivedi
Citations in the corpus
- Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, methodology): “Geometric matrix completion An alternative relaxation of the rank operator in (1) is to constraint the space of solutions to be smooth w.r.t. some geometric structure of the matrix rows and columns (Ma et al., 2011; Kalofolias et al., 2014; Rao et al., 2015; Benzi et al., 2016).”
- Fast Robust PCA on Graphs (2015, background): “For NMF and GNMF only the unit-norm normalization is applied to all the samples of the dataset.”