基于大数据技术和 DSO 算法的改进型智能机器学习音乐推荐方法

Sujie He, Yuxian Li
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引用次数: 0

摘要

引言:为提升用户使用音乐服务的体验质量,提高音乐推荐平台的效率,研究精准高效的音乐推荐方法,构建精准的实时在线推荐平台是优质音乐网站平台成功的关键点:方法:利用大数据和智能优化算法改进深度置信网络,构建音乐推荐算法。首先,通过分析音乐推荐算法的原理,提取音乐特征,同时提出音乐推荐算法的评价指标;然后,结合深度睡眠优化算法,提出基于改进深度置信网络的音乐推荐方法;最后,通过仿真实验分析,验证了所提方法的有效性。结果:在满足实时性要求的同时,提出的方法提高了音乐推荐的准确率、召回率和覆盖率。结论:解决了当前音乐推荐算法中信号特征捕捉不全、分类效率不足、泛化能力差等问题。
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An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO Algorithms
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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