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

January 08, 2016 · Entered Twilight · 🏛 IEEE International Conference on Acoustics, Speech, and Signal Processing

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Repo contents: .editorconfig, .gitignore, .travis.yml, AUTHORS.rst, CONTRIBUTING.rst, GPLv3.txt, HISTORY.rst, LICENSE, MANIFEST.in, Makefile, README.rst, docs, notebook, recog, requirements.txt, resources, setup.cfg, setup.py, tests, tox.ini

Authors Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst arXiv ID 1601.01892 Category stat.ML: Machine Learning (Stat) Cross-listed cs.IR, cs.LG, physics.data-an Citations 53 Venue IEEE International Conference on Acoustics, Speech, and Signal Processing Repository https://github.com/kikohs/recog ⭐ 8 Last Checked 1 month ago

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.