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

Authors

  • Sujie He Shandong Unveristy of Art
  • Yuxian Li Jinan Technician College

DOI:

https://doi.org/10.4108/eetsis.5176

Keywords:

Music recommendation method, deep confidence network, music feature extraction, deep sleep algorithm

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.

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Published

08-04-2024

How to Cite

1.
He S, Li Y. An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms. EAI Endorsed Scal Inf Syst [Internet]. 2024 Apr. 8 [cited 2024 May 18];11(5). Available from: https://publications.eai.eu/index.php/sis/article/view/5176