Research and Application of Music Personalized Recommendation System Based on Random Forest Algorithm

Abstract

In recent years, with the advent of the era of big data and the rise of data mining technology, people used to waste a lot of time looking for advantageous information, which was very inefficient. Music is an indispensable part of everyone’s daily life, and major music platforms are gradually emerging. Finding music that users like is the key to attracting users. At present, there are relatively few studies on personalized music recommendation using data mining algorithms. In this paper, the random forest algorithm is used to pre-process the collected data, and then the data is modeled, the model parameters and fitting coefficients are adjusted, the appropriate index is selected as the learning parameter, and the learning curve is obtained at the same time. Finally, the prediction is carried out through examples, and it is found that the prediction accuracy is high, and the WeChat applet is used for development and application. The approach in this article is simple and constructive, and it is dedicated to recommending personalized and exclusive music for particular users, so that you can spend every day happily.