Music Artist Recommendation System Based on Listening History Using SVD and MICE Imputation Approaches

In the digital era, music streaming platforms face challenges in providing relevant music recommendations to users. This research aims to develop a music artist recommendation system based on the user's listening history using the SVD and MICE methods. In this research, MICE was applied together with ALS predictive model. SVD is used to identify latent patterns between users and artists, while MICE address the problem of missing data in listening history. The data used comes from the online music platform Last.fm. Analysis was carried out with Julia 1.8.5 software. The results show that the model with MICE provides more accurate and consistent recommendations compared to SVD, especially in the context of missing data. Accuracy using the MICE model provides results of up to 96%, while the SVD model provides an accuracy of 90,22%. This approach can increase the relevance of recommendations, helping users find artists according to their preferences. These findings support the application of MICE in music recommendation systems, with the potential to improve user experience on music streaming platforms.