A Hybrid Music Recommendation System Based on K-Means Clustering and Multilayer Perceptron

Rafael Cintra de Araúijo, Victor Moisés Silveira Santos, João Fausto Lorenzato de Oliveira, Alexandre M. A. Maciel

2025

Abstract

Music recommendation systems have become indispensable tools for enhancing user experiences by offering personalized playlists tailored to individual preferences. However, traditional recommendation approaches often struggle with challenges such as accurately capturing user tastes, maintaining diversity in recommendations, and addressing the cold-start problem, where limited user data hampers effective predictions. To address these issues, this study presents a hybrid recommendation model that integrates K-Means clustering and a Multilayer Perceptron (MLP) neural network to deliver coherent and diverse music recommendations. The model utilizes the all-MiniLM-L6-v2 embedding, a powerful sentence-transformer, to analyze semantic similarities in textual data such as song titles, artist names, and lyrics, encoding them into a dense vector space. Combined with normalized audio features, these embeddings enable clustering and similarity-based recommendations. Extensive experiments, conducted on datasets from Spotify and Kaggle, employed key metrics such as accuracy, F1 score, silhouette score, and cosine similarity to evaluate performance. The results highlight the system’s ability to maintain genre coherence and acoustic feature consistency, minimize track repetition, and foster user engagement. Addressing challenges like the cold-start problem and diverse user preferences, the proposed model demonstrates its potential for real-world applications. Future extensions include incorporating user feedback and supporting multi-session recommendations to adapt to evolving music trends, offering a robust and innovative approach to music recommendation systems.

Paper Citation

in Harvard Style

de Araúijo R., Santos V., Lorenzato de Oliveira J. and Maciel A. (2025). A Hybrid Music Recommendation System Based on K-Means Clustering and Multilayer Perceptron. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 335-342. DOI: 10.5220/0013436700003929

in Bibtex Style

@conference{iceis25,
author={Rafael de Araúijo and Victor Santos and João Lorenzato de Oliveira and Alexandre Maciel},
title={A Hybrid Music Recommendation System Based on K-Means Clustering and Multilayer Perceptron},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={335-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013436700003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Hybrid Music Recommendation System Based on K-Means Clustering and Multilayer Perceptron
SN - 978-989-758-749-8
AU - de Araúijo R.
AU - Santos V.
AU - Lorenzato de Oliveira J.
AU - Maciel A.
PY - 2025
SP - 335
EP - 342
DO - 10.5220/0013436700003929
PB - SciTePress