<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the rhythm of music. As an effective relief method, music intervention has far-reaching significance in the development of psychotherapy. In the practice of music therapy, music selection is particularly important, and the rationality and rigor of music selection directly affect the therapeutic effect. The process of music selection in music therapy is in common with Internet music recommendation, so it has theoretical and practical value to apply personalized music recommendation algorithms to music therapy. In this paper, driven by big data, a music recommendation model based on an improved collaborative filtering (CF) algorithm is proposed, which combines the psychological adjustment of users’ music preferences and different music rhythm features to select music for music therapy and provides theoretical support for music therapy selection. The results show that the construction of music resources for music therapy based on an improved CF algorithm can greatly improve the music selection process of music therapy.</p>
</abstract>

Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the rhythm of music. As an effective relief method, music intervention has far-reaching significance in the development of psychotherapy. In the practice of music therapy, music selection is particularly important, and the rationality and rigor of music selection directly affect the therapeutic effect. The process of music selection in music therapy is in common with Internet music recommendation, so it has theoretical and practical value to apply personalized music recommendation algorithms to music therapy. In this paper, driven by big data, a music recommendation model based on an improved collaborative filtering (CF) algorithm is proposed, which combines the psychological adjustment of users’ music preferences and different music rhythm features to select music for music therapy and provides theoretical support for music therapy selection. The results show that the construction of music resources for music therapy based on an improved CF algorithm can greatly improve the music selection process of music therapy.

Music Therapy Music Selection Based on Big Data Analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Music Therapy Music Selection Based on Big Data Analysis"}

Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the...

Music Therapy Music Selection Based on Big Data Analysis

Online veröffentlicht: 10. Apr. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 22. Feb. 2024

DOI: https://doi.org/10.2478/amns-2024-0019

Schlüsselwörter
Big data, Music recommendation, Music therapy, Music selection

© 2024 Chuyi Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Music Therapy Music Selection Based on Big Data Analysis

Online veröffentlicht: 10. Apr. 2024

Seitenbereich: -

Eingereicht: 15. Jan. 2024

Akzeptiert: 22. Feb. 2024

DOI: https://doi.org/10.2478/amns-2024-0019

SchlüsselwörterBig data, Music recommendation, Music therapy, Music selection

© 2024 Chuyi Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data, Music recommendation, Music therapy, Music selection