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

<p>The current music recommendation is less efficient and cannot meet the needs of most people. This paper combines deep learning and migration learning to research music recommendations. It mainly utilizes the hybrid recommendation method of label recommendation and noise reduction autoencoder to achieve accurate music recommendations after extracting the features of music and the user’s preference features. In addition, in order to explore the recommendation effect of the model in this paper, the deviation between the measured score and the actual score of this model is compared and analyzed as well as the similarity difference of music with different features is explored in terms of HR, Recall, and NDCG index performance. The results show that the number of songs with a deviation of 2 or less in the predicted scores of this recommender system all account for 88% of the total number of songs, which is better than other models. In terms of HR, Recall, and NDCG, this paper’s model has better recommendation performance than other models, and the similarity of music recommendation between the same album and different albums is more than 0.65. This study is of great significance for eliminating the information barriers to enhance and explore the value of music data information.</p>
</abstract>

The current music recommendation is less efficient and cannot meet the needs of most people. This paper combines deep learning and migration learning to research music recommendations. It mainly utilizes the hybrid recommendation method of label recommendation and noise reduction autoencoder to achieve accurate music recommendations after extracting the features of music and the user’s preference features. In addition, in order to explore the recommendation effect of the model in this paper, the deviation between the measured score and the actual score of this model is compared and analyzed as well as the similarity difference of music with different features is explored in terms of HR, Recall, and NDCG index performance. The results show that the number of songs with a deviation of 2 or less in the predicted scores of this recommender system all account for 88% of the total number of songs, which is better than other models. In terms of HR, Recall, and NDCG, this paper’s model has better recommendation performance than other models, and the similarity of music recommendation between the same album and different albums is more than 0.65. This study is of great significance for eliminating the information barriers to enhance and explore the value of music data information.

Music personalization imputation method based on deep transfer transfer learning

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Music personalization imputation method based on deep transfer transfer learning"}

The current music recommendation is less efficient and cannot meet the needs of most people. This paper combines deep learning and migration learning to...

Music personalization imputation method based on deep transfer transfer learning

Data publikacji: 15 maj 2024

Zakres stron: -

Otrzymano: 29 sty 2024

Przyjęty: 13 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1128

Słowa kluczowe
Migration learning, Personalized recommendation, Similarity difference, Autoencoder, Preference analysis

© 2024 Jing Wang, published by Sciendo

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

Music personalization imputation method based on deep transfer transfer learning

Data publikacji: 15 maj 2024

Zakres stron: -

Otrzymano: 29 sty 2024

Przyjęty: 13 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1128

Słowa kluczoweMigration learning, Personalized recommendation, Similarity difference, Autoencoder, Preference analysis

© 2024 Jing Wang, published by Sciendo

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

Słowa kluczowe
Migration learning, Personalized recommendation, Similarity difference, Autoencoder, Preference analysis