Development of a Music Recommendation System Using Machine Learning Algorithms

Thesis Abstract

Abstract
This thesis presents the development of a Music Recommendation System utilizing Machine Learning Algorithms to enhance user experience in discovering new music. The project aims to address the challenges faced by music streaming platforms in providing personalized recommendations to users, thereby improving user engagement and satisfaction. The study involves the exploration and implementation of various machine learning techniques to analyze user listening behavior and preferences, with the ultimate goal of delivering accurate and relevant music recommendations. The research begins with a comprehensive introduction that outlines the background of the study and highlights the problem of ineffective music recommendations on existing platforms. The objectives of the study are defined to guide the research process, along with the limitations and scope of the study. The significance of the study is emphasized, emphasizing its potential impact on the music streaming industry. The structure of the thesis is also outlined to provide a roadmap for the reader. A detailed literature review is conducted in Chapter Two, which covers ten key areas related to music recommendation systems, machine learning algorithms, user modeling, collaborative filtering, content-based filtering, and hybrid recommendation approaches. The review synthesizes existing knowledge and identifies gaps in current research, providing a foundation for the development of the proposed Music Recommendation System. Chapter Three focuses on the research methodology employed in this study, detailing the data collection process, dataset preparation, feature engineering, model selection, training, and evaluation. The methodology includes the use of popular machine learning algorithms such as collaborative filtering, matrix factorization, and deep learning models to build the recommendation system. Chapter Four presents an in-depth discussion of the findings obtained from the implementation and evaluation of the Music Recommendation System. The performance metrics, including precision, recall, and accuracy, are analyzed to assess the effectiveness of the system in providing personalized music recommendations. The results demonstrate the potential of machine learning algorithms in improving the quality of music recommendations and enhancing user satisfaction. Finally, Chapter Five provides a summary of the project thesis, highlighting the key contributions, implications, and future directions for research. The conclusion reflects on the achievements of the study and offers recommendations for further enhancements to the Music Recommendation System. Overall, this research contributes to the advancement of personalized music recommendation systems using machine learning algorithms, with the potential to revolutionize the way users discover and enjoy music in the digital age.