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

<p>In this study, we employed a Deep Belief Network-Deep Neural Network (DBN-DNN) model to perform personalized data analysis based on user-specific music preferences and listening behavior. This approach seeks to transcend informational boundaries and enhance the exploration and realization of the intrinsic value of data. We conducted a regression correlation analysis on a comprehensive dataset to investigate the potential relationship between college students’ music preferences and their personality traits, considering both musical and psychological dimensions. The study revealed that pop music (mean rating of 3.89), classical music (2.97), and hip-hop music (2.13) ranked highest in popularity among college students. Additionally, a significant negative correlation was observed between the preference for tension-rebellion-themed music in adulthood and the family’s socioeconomic status during childhood (-0.357). Furthermore, there was a notable positive correlation between openness to experience and preferences for classical music, popular music, and blues music (-0.864). A positive correlation was also evident between conscientiousness and preferences for classical music, light music (0.834), and traditional Chinese music. Thus, the DBN-DNN model coupled with regression analysis effectively elucidates the relationship between music preferences and psychological traits.</p>
</abstract>

In this study, we employed a Deep Belief Network-Deep Neural Network (DBN-DNN) model to perform personalized data analysis based on user-specific music preferences and listening behavior. This approach seeks to transcend informational boundaries and enhance the exploration and realization of the intrinsic value of data. We conducted a regression correlation analysis on a comprehensive dataset to investigate the potential relationship between college students’ music preferences and their personality traits, considering both musical and psychological dimensions. The study revealed that pop music (mean rating of 3.89), classical music (2.97), and hip-hop music (2.13) ranked highest in popularity among college students. Additionally, a significant negative correlation was observed between the preference for tension-rebellion-themed music in adulthood and the family’s socioeconomic status during childhood (-0.357). Furthermore, there was a notable positive correlation between openness to experience and preferences for classical music, popular music, and blues music (-0.864). A positive correlation was also evident between conscientiousness and preferences for classical music, light music (0.834), and traditional Chinese music. Thus, the DBN-DNN model coupled with regression analysis effectively elucidates the relationship between music preferences and psychological traits.

Deep learning models were used to analyze the relationship between music preference and radiability psychology characteristics

Applied Mathematics and Nonlinear Sciences

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

In this study, we employed a Deep Belief Network-Deep Neural Network (DBN-DNN) model to perform personalized data analysis based on user-specific music...

Deep learning models were used to analyze the relationship between music preference and radiability psychology characteristics

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 12. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1222

Schlüsselwörter
DBN-DNN model, Music preference, Regression analysis, Personalized recommendation

© 2024 Zi Ye, published by Sciendo

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

Deep learning models were used to analyze the relationship between music preference and radiability psychology characteristics

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 12. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1222

SchlüsselwörterDBN-DNN model, Music preference, Regression analysis, Personalized recommendation

© 2024 Zi Ye, published by Sciendo

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

Schlüsselwörter
DBN-DNN model, Music preference, Regression analysis, Personalized recommendation