Title: An adaptive recommendation method for music resources based on LDA-MURE model

Authors: Xiaoxing Lu

Addresses: The Department of Music and Dance, Luohe Vocational Technology College, Luohe, Henan, China

Abstract: To improve the coverage of recommendation results and reduce the MEA value, the paper proposes an adaptive recommendation method for music resources based on LDA-MURE model. Firstly, remove and reduce the dimensionality of music resource data, and then integrate multimodal feature fusion methods with attention networks to classify the processed music resources. Then, the strength of emotional energy and emotional positivity are introduced to estimate the human emotional state. Finally, the distribution and emotional state estimation results of different types of music themes are input into LDA-MURE model, and music resources are recommended for users based on their emotional states. According to the experiment, MEA index value of this method is always controlled within 0.2, with a coverage rate between 0.944 and 0.971, indicating that the application of this method can recommend more music resources of different types and styles, and the accuracy of the recommendation results is high.

Keywords: music resources; noise reduction processing; dimension reduction treatment; feature classification; user emotions; adaptive recommendation.

DOI: 10.1504/IJCAT.2024.141368

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.115 - 124

Received: 11 Dec 2023
Accepted: 12 Apr 2024
Published online: 09 Sep 2024 *

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