Personalized Music Recommendation System Based on Machine Learning and Collaborative Filtering

There is an abundance of music content available to listeners across numerous platforms as a result of the music industry's digital transition. Users have the problem of navigating this huge musical landscape to find songs that resonate with their unique preferences when they have millions of songs at their fingertips. This paper presents a machine learning-based music recommendation system. The system takes into account various factors such as user preferences and music genre. The recommendation system is based on recent advancements in machine learning and uses collaborative filtering to provide personalized music recommendations for each user. The system is tested using publicly available datasets and achieves satisfactory results. The paper first provides an overview of existing music recommendation systems and their shortcomings. It then outlines the principles of machine learning to explain the process of personalized music recommendation. It describes the various stages in the recommendation process such as data collection, data preprocessing, feature selection, model selection, model training, model testing, and validation. The system is tested with publicly available datasets and the results are compared with existing recommendation systems. The results show that the proposed system achieves a satisfactory performance in comparison to the existing recommendation systems. Furthermore, it is observed that the system is able to recommend personalized music to users.