# Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

arXiv: https://arxiv.org/abs/1601.01892 · HTML: https://mlsanity.com/paper/1601.01892/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2016.

## Built on by (most cited first, 4 of 4)

- [Spectrally Pruned Gaussian Fields with Neural Compensation](https://mlsanity.com/paper/2405.00676/) (2024)
- [Automating Nearest Neighbor Search Configuration with Constrained Optimization](https://mlsanity.com/paper/2301.01702/) (2023)
- [Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation](https://mlsanity.com/paper/2207.04471/) (2022)
- [Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution](https://mlsanity.com/paper/2302.03933/) (2023)

## Abstract (arXiv)

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.