Journal's Facebook pageAudio Feature-based User Profiles for Personalized Music Recommendation: A Dataset-driven Evaluation
- University of Craiova, Craiova, Romania
neremzoiu.ionut.k4c@student.ucv.ro, andreea.badica121@yahoo.com
Abstract
In this paper, we propose an experimental content-based track recommendation system, which relies on an aggregated features which considers audio feature values from Spotify Data Catalog, track lyrics and popularity ratings. As system evaluation protocol, we evaluate how relevant the top-5 recommended tracks are to the user profile, which is based on average audio feature (danceability, energy, valence, loudness, instrumentalness, liveness, speechiness and acousticness) values from the user’s listening history. Based on the evaluation results, we come to the conclusion that the recommended tracks are similar to the user’s profile.
Key words
recommender systems, pca, k-means, aggregated feature, mean absolute error
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS250418083N
Publication information
Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Neremzoiu, I., Bădică, A. L.: Audio Feature-based User Profiles for Personalized Music Recommendation: A Dataset-driven Evaluation. Computer Science and Information Systems, Vol. 23, No. 1, 585-601. (2026), https://doi.org/10.2298/CSIS250418083N
