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I
摘 要
数字化时代带动着整个社会的信息化发展,随着数字媒体的不断发展,现在
通多媒体数字产品的内容越来越丰富,传播影响力越来越强,以音乐为例,现在
的音乐文化多样、音乐资源也异常的丰富,在这种大数据的环境下,人们要想找
到想要的音乐类型、找到心里所想的那首音乐无疑是大海捞针。现在音乐的推荐
系统也非常的多,但是推荐的内容、推荐的方式却与用户的感知差距明显,或多
或少都会存在一些问题。而随着深度学习、卷积神经网络的不断发展,现在的深
度学习在图像识别、自然语言等领域都有着很好的发展,也很好的应用在了音乐
的推荐过程中。
本次的研究是基于使用自动编码器,通过与卷积神经网络相结合,以挖掘音
频、歌词本身的非线性特征,来实现很好的音乐推荐、音乐查找识别的功能实现,
并将内容特征与协同过滤共同作用,训练紧耦合模型。通过此次的系统搭建与开
发,能够通过深度学习的方式让系统可以实现按照用于的喜好来进行音乐的推荐
的功能实现。
关 键 词:深度学习;音乐推荐;Python;KNNBaseline;
II
ABSTRACT
The digital era is driving the information development of the whole society. With
the continuous development of digital media, the content of multimedia digital products
is becoming more and more rich, and the communication influence is becoming
stronger and stronger. Take music as an example. Today's music culture is diverse, and
the music resources are also unusually rich. In this big data environment, it is
undoubtedly a needle in a haystack if people want to find the type of music they want
and the music they want. Now there are many music recommendation systems, but the
content and way of recommendation are obviously different from the user's perception,
and there are more or less problems.
This research is based on the use of automatic encoder, combined with
convolutional neural network, to mine the non-linear characteristics of audio and lyrics,
to achieve good music recommendation, music search and recognition functions, and
to train a tightly coupled model by combining content features with collaborative
filtering. Through this system construction and development, the system can realize the
function of music recommendation according to the preferences of the users through
in-depth learning.
Key words: deep learning; Music recommendation; Python; KNNBaseline
III
目 录
摘 要.....................................................................................................................I
ABSTRACT ..........................................................................................................II
1、绪论 ..................................................................................................................5
1.1 研究背景 ......................................................................................................5
1.2 研究现状 ......................................................................................................5
1.3 研究的内容 ..................................................................................................6
1.4 开发的技术介绍 ..........................................................................................6
1.4.1Python 技术............................................................................................6
1.4.2MySQL 数据库......................................................................................7
1.4.3B/S 结构 .................................................................................................7
1.5 论文的结构 ..................................................................................................7
2 深度学习的算法研究 .........................................................................................8
2.1 卷积神经网络介绍 ......................................................................................8
2.1.1 卷积神经网络特性 ...............................................................................8
2.1.2 卷积的方式 ...........................................................................................8
2.2 基本内容推荐算法 ......................................................................................8
2.3 基于协同过滤的推荐算法 ..........................................................................9
2.4 深度学习技术相关概念 ............................................................................10
2.5 深度学习技术推荐算法 ............................................................................10
2.6KNNBaseline 算法......................................................................................11
3 基于深度学习的音乐推荐系统算法需求 .......................................................12
3.1 需求设计 ....................................................................................................12
3.2 可行性分析 ................................................................................................12
3.2.1 技术可行性 .........................................................................................12
3.2.2 经济可行性 .........................................................................................12
3.2.3 操作可行性 .........................................................................................12
3.3 其他功能需求分析 ....................................................................................13
4 系统设计 ...........................................................................................................14
4.1 系统的整体设计 ........................................................................................14
4.2 数据库的设计 ............................................................................................14
5 系统的实现 .......................................................................................................16
5.1 系统的首页 ................................................................................................16
5.2 音乐播放界面的实现 ................................................................................16
5.3 音乐推荐功能的实现 ................................................................................17
5.4 后台管理系统的实现 ................................................................................18
IV
6 系统的测试 .......................................................................................................19
6.1 测试的目的 ................................................................................................19
6.2 测试的内容 ................................................................................................19
6.3 测试的结果 ................................................................................................19
结论 ......................................................................................................................20
参考文献 ..............................................................................................................21
致谢 ......................................................................................................................22
剩余21页未读,继续阅读
资源评论
- zhaozhao_232023-11-06这个资源值得下载,资源内容详细全面,与描述一致,受益匪浅。
- vaa4532024-04-02发现一个宝藏资源,资源有很高的参考价值,赶紧学起来~
- jxnu-csdn2024-05-15资源很受用,资源主总结的很全面,内容与描述一致,解决了我当下的问题。
- 余周周的小迷妹2024-05-03资源很赞,希望多一些这类资源。
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