Research on Traditional Music Recommendation Methods Based on the MIFS Algorithm

With the rapid development of digital music platforms, personalized music recommendation systems have become essential for enhancing user experience by tailoring content to individual tastes and preferences. This paper explores the application of the Mutual Information Feature Selection (MIFS) algorithm in the context of traditional music recommendation systems. The MIFS algorithm, a widely-used feature selection method, is employed to identify the most relevant features from large and diverse music datasets. By selecting key features that best represent user preferences and music characteristics, the algorithm ensures that the recommendations align more closely with users’ tastes. The study begins by examining the core principles and advantages of the MIFS algorithm, including its ability to effectively filter out irrelevant or redundant information, thereby improving the efficiency and accuracy of the recommendation system. The paper then integrates the MIFS algorithm into a recommendation framework tailored for traditional music, an area that has received comparatively less attention than modern music genres. Using several traditional music datasets, the performance of the MIFS-based recommendation system is evaluated against conventional methods. The results demonstrate that the MIFS-based approach significantly improves both the accuracy of recommendations and user satisfaction. This research contributes to the development of more effective, personalized recommendation systems for traditional music, offering valuable insights for future applications in digital music platforms.