"基于深度学习的音乐推荐系统设计与实现"

In the era of digitalization, the society is experiencing a rapid transformation towards digital media, with the content of multimedia digital products becoming increasingly rich and influential. Taking music as an example, the music culture is diverse and the resources are abundant. In this era of big data, it can be like finding a needle in a haystack for people to discover the type of music they like or find that particular song they are looking for. Although there are numerous music recommendation systems available today, there are still noticeable gaps between the recommended content and the user's perception, leading to various issues. With the continuous advancement of deep learning and convolutional neural networks, deep learning has shown great progress in fields such as image recognition and natural language processing, and has also been effectively applied in the music recommendation process. This research is based on the use of autoencoders combined with convolutional neural networks to explore the non-linear features of audio and lyrics, in order to achieve effective music recommendation and recognition functions. By integrating content features with collaborative filtering, a tightly coupled model is trained. Through the development and implementation of this system, the recommendation of music based on user preferences can be realized through deep learning. The keywords for this research include deep learning, music recommendation, Python, and KNNBaseline. Overall, this study focuses on utilizing deep learning techniques to enhance music recommendation systems, addressing the challenges and discrepancies present in current systems. By leveraging the power of deep learning algorithms, the system aims to provide more accurate and personalized music recommendations to users, ultimately improving the overall music listening experience in the digital age.