基于大数据的音乐流行趋势预测及推荐分析毕业论文+项目源码+爬虫源码+网页端源码+数据库sql文件+部署说明+演示视频_音乐流行趋势预测源代码

作者：小蓝xlanll | 2024-03-16 15:22:50

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音乐流行趋势预测源代码

基于大数据的音乐流行趋势预测及推荐分析

摘要

基于机器学习构建音乐流行趋势预测模型仅使用了离预测目标时间段较近范围的数据。本文对歌曲聚类后进行分组实验：以模糊集理论为基础，分解时间信息粒，构建“triangle”模型；采用SVM预测triangle模型的low，R，up参数，可得到准确的短时空间和趋势变化。这对于平台中原创行为、使用行为以及运营商的营销活动都有重要的指引作用。

系统实现用户对音乐评分的搜集（Python爬虫爬取数据），后端使用大教据推荐算法构造，前端使用MVC框架搭建大数据音乐推荐系统。系统教据序使用了关系型教据库MySQL。前端收集过用户行为数据后传到后端使用基于用户的协同过滤算法来推荐出用户可能喜欢的音乐。采用BS架构，使用Java程序设计语言、MySQL数据库、Hadoop离线分析、 java 开源工具Eclipse编写程序、Java的JRE运行环境、JSP页面等工具开发而来。

音乐流行趋势预测为企业预测未来流行音乐走向、预判黑马音乐提供了理论支撑和决策上的技术支持，具有重要的商业价值。相比传统预测方法，基于机器学习构建预测模型不仅理论完善，而且在实践中有强大的鲁棒性和泛化能力，可直接移植到平台中应用。

Music pop trend prediction and recommendation analysis based on big data

Abstract

The music pop trend prediction model construction based on machine learning uses only the data close to the predicted target time period. In this paper, the grouping experiment is conducted after song clustering: based on the fuzzy set theory, decompose the time information particles and construct the "triangle" model; to predict the low, R and up parameters of the triangle model by SVM, we can get accurate short-time space and trend change. This is an important guiding role in the original behavior, usage behavior and carrier marketing activities on the platform.

The system realizes the collection of music score (Python crawler crawl data), the back end uses the big teaching data recommendation algorithm construction, and the front end uses the MVC framework to build the big data music recommendation system. The systematic reference order uses the relational reference library MySQL. The front end collected user behavior data and passed it to the back end using a user-based collaborative filtering algorithm to recommend music that the user may like. The BS architecture was developed by using the Java programming language, MySQL database, Hadoop offline analysis, java open source tool Eclipse programming, Java's JRE running environment, JSP page and other tools.

Music pop trend forecast provides theoretical support and decision-making technical support for enterprises to predict the future pop music trend and predict the dark horse music, which has important commercial value. Compared with traditional prediction methods, building prediction models based on machine learning is not only theoretically perfect, but also has strong robustness and generalization ability in practice, which can be directly transplanted to the platform for application.

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