2016/01/08 by Kirell Benzi, Benzi, Kirell, Vassilis Kalofolias +5
Computer Science · Mathematics · Physics and Astronomy · #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music Technology and Sound Studies #Music and Audio Processing #Recommender Systems and Techniques #Statistics and Probability (physics.data-an) #cs.IR #cs.LG #physics.data-an #stat.ML
paper · pdf · doi:10.48550/arxiv.1601.01892
Code available at: https://github.com/kikohs/recog
openalex publication_date 2016/01/08 · arxiv created 2016/01/13 · arxiv updated 2016/01/14 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
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.