Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Yan Chen

doi:10.54254/2755-2721/87/20241564

Research Article

Open access

Published on 14 August 2024

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Chen,Y. (2024). Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques. Applied and Computational Engineering,87,197-202.

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Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Yan Chen *^,1,

¹ Wuhan Conservatory of Music, Hubei, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/87/20241564

Abstract

Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into hybrid recommendation systems, combining collaborative and content-based approaches using advanced models such as neural networks and hybrid autoencoders. Our finding show that, hybrid systems provide the most accurate and personalize recommendations, albeit requiring significant computational resources. Practical applications from platforms likes Spotify and Pandora illustrate the effectiveness of these approaches in real-world settings.

Keywords

Music Information Retrieval, Music Recommendation Systems, Machine Learning, Data Mining, Collaborative Filtering

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References

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Cite this article

Chen,Y. (2024). Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques. Applied and Computational Engineering,87,197-202.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/

ISBN：978-1-83558-585-6(Print) / 978-1-83558-586-3(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.87

ISSN：2755-2721(Print) / 2755-273X(Online)

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