A Survey of Music Recommendation Systems
Zhaodian Zeng, Yining Wang, Yanbin Zhao, Wenxuan Shi · 2024
The contradiction between users' music consumption and the abundance of music data has spurred the emergence of music recommendation systems. These systems merge recommendation systems with music suggestions, serving as a widely utilized solution in real-world scenarios and presenting a multifaceted challenge. Many methods of recommendation systems need to combine the characteristics of music recommendation itself. For example, with the rise of mobile Internet and the popularity of smartphones, the places where users listen to music are not limited by time or space; the emotions of users when listening to music also have a great influence on music recommendations, so it is necessary to study personalized music recommendation systems. In this review, we mainly focus on deep learning-based music recommendation systems, starting from the perspectives of neural network structure and personalized recommendation systems, discussing the principles, characteristics, and applications of different models. We also review the main methods and latest research results used in music recommendation systems. This review should be of great help to scholars who are new to the field of music recommendation or want to quickly grasp the recent advances in the field.