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Research Article

An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms

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  • @ARTICLE{10.4108/eetsis.5176,
        author={Sujie He and Yuxian Li},
        title={An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={5},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={4},
        keywords={Music recommendation method, deep confidence network, music feature extraction, deep sleep algorithm},
        doi={10.4108/eetsis.5176}
    }
    
  • Sujie He
    Yuxian Li
    Year: 2024
    An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms
    SIS
    EAI
    DOI: 10.4108/eetsis.5176
Sujie He1,*, Yuxian Li2
  • 1: Shandong Unveristy of Art
  • 2: Jinan Technician College
*Contact email: z00807@sdca.edu.cn

Abstract

INTRODUCTION: In an effort to enhance the quality of user experience in using music services and improve the efficiency of music recommendation platforms, researching accurate and efficient music recommendation methods and constructing an accurate real-time online recommendation platform are the key points for the success of a high-quality music website platform. OBJECTIVES: To address the problems of incomplete signal feature capture, insufficient classification efficiency and poor generalization of current music recommendation methods. METHODS: Improve the deep confidence network to construct music recommendation algorithm by using big data and intelligent optimization algorithm. Firstly, music features are extracted by analyzing the principle of music recommendation algorithm, and evaluation indexes of music recommendation algorithm are proposed at the same time; then, combined with the deep sleep optimization algorithm, a music recommendation method based on improved deep confidence network is proposed; finally, the efficiency of the proposed method is verified through the analysis of simulation experiments. RESULTS: While meeting the real-time requirements, the proposed method improves the music recommendation accuracy, recall, and coverage. CONCLUSION: Solves the questions of incomplete signal feature capture, insufficient classification efficiency, and poor generalization of current music recommendation algorithms.

Keywords
Music recommendation method, deep confidence network, music feature extraction, deep sleep algorithm
Received
2024-02-22
Accepted
2024-03-27
Published
2024-04-08
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.5176

Copyright © 2024 S. He et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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