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Music Intelligent Push Play and Data Analysis System Based on 5G Internet of Things

机译:基于5G的物联网的音乐智能推动和数据分析系统

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With the rapid development of information science today, multifunctional and intelligent applications have gradually become the focus of attention. In the data management system, the first consideration is the reliability of the data source, followed by the intelligent processing after the data are collected. Due to the upgrade of the Internet to the Internet of Things, the way of network information transmission has also become a problem that people need to think about. The transmission mode of network information services will be converted from the passive transmission of information by traditional servers to the form of actively pushing information. The application of intelligent push technology in the field of the Internet of Things is a prominent and important direction in the development of the Internet of Things. This article mainly introduces the research on the intelligent music push and data analysis system based on the 5G Internet of Things, with the intention of providing some ideas and directions for the research of the music intelligent push and play and data analysis system. This paper proposes a research method for music intelligent push playback and data analysis system based on 5G Internet of Things, including current intelligent push related technologies, music evaluation matrix, user dissimilarity matrix, and music feature similarity calculation. The experimental results in this paper show that with the increase in the number of users, the accuracy of the recommended results of the system under the Hadoop framework gradually stabilizes, eventually reaching 91.2%.
机译:随着当今信息科学的快速发展,多功能和智能应用程序逐渐成为关注的焦点。在数据管理系统中,第一次考虑是数据源的可靠性,后跟数据收集后的智能处理。由于互联网升级到互联网上,网络信息传输的方式也成为人们需要思考的问题。网络信息服务的传输模式将由传统服务器的信息的被动传输转换为主动推动信息的形式。智能推动技术在互联网领域的应用是事物互联网发展的突出和重要方向。本文主要介绍了基于5G内容互联网的智能音乐推送和数据分析系统的研究,意图为音乐智能推动和数据分析系统的研究提供一些思路和方向。本文提出了一种基于5G互联网的音乐智能推回播放和数据分析系统的研究方法,包括当前智能推动相关技术,音乐评估矩阵,用户异化矩阵和音乐特征相似性计算。本文实验结果表明,随着用户数量的增加,在Hadoop框架下,系统推荐结果的准确性逐渐稳定,最终达到91.2%。

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