"基于Python Django深度学习的音乐推荐系统研究与开发"

本研究基于Python Django深度学习的音乐推荐方法，致力于解决音乐推荐系统中存在的问题。随着数字化时代的发展，音乐文化多样，音乐资源丰富，但是用户要找到符合自己口味的音乐却是一项挑战。现有的音乐推荐系统存在内容和方式与用户感知差距明显的问题。通过结合自动编码器和卷积神经网络，本研究挖掘音频、歌词的非线性特征，实现音乐推荐、查找识别的功能。内容特征与协同过滤相结合，训练紧耦合模型，使系统更好地按照用户喜好进行音乐推荐。通过深度学习方式，本系统实现了个性化音乐推荐功能。ABSTRACTThe 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 abundant, and their influence is becoming stronger. Taking music as an example, the current music culture is diverse, and the music resources are extremely rich. In this era of big data, it is like finding a needle in a haystack to find the desired music type or the desired music. There are many music recommendation systems now, but the recommended content and methods are significantly different from users' perceptions, and there may be some problems more or less. With the continuous development of deep learning and convolutional neural networks, deep learning now has good development in image recognition, natural language, and is also well applied in the music recommendation process. This study is based on using autoencoders combined with convolutional neural networks to explore the nonlinear features of audio and lyrics, to achieve good music recommendation and identification functions. Content features and collaborative filtering work together to train a tightly coupled model. Through the development of this system, it is possible to achieve music recommendation based on user preferences through deep learning. Key words: deep learning; music recommendation; Python; KNNBaseline.