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

TL;DR

This paper introduces a hybrid song recommender system that combines Non-negative Matrix Factorization and graph-based total variation to improve recommendation accuracy by leveraging collaborative filtering and content features.

Contribution

It formulates a novel matrix completion approach integrating NMF and graph total variation, outperforming existing methods on real-world data.

Findings

Outperforms models based solely on low-rank or graph information

Effectively combines collaborative and content-based filtering

Demonstrates versatility across different evaluation metrics

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.