Development of an AI-powered Music Recommendation System

Thesis Abstract

Abstract
This thesis presents the research and development of an AI-powered music recommendation system that aims to enhance user experience and engagement in music streaming platforms. The exponential growth of digital music consumption has led to an overwhelming amount of music content available to users, making it challenging for them to discover new music that aligns with their preferences. Traditional recommendation systems often rely on collaborative filtering and content-based approaches, which have limitations in providing accurate and diverse music recommendations. Therefore, the proposed AI-powered system leverages machine learning algorithms and natural language processing techniques to analyze user preferences and music content, in order to deliver personalized and relevant music recommendations. The project begins with a comprehensive introduction that outlines the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. A detailed literature review in Chapter Two explores existing research on music recommendation systems, AI technologies, and user preferences in the context of music streaming platforms. The review highlights the gaps and challenges in current recommendation systems, setting the foundation for the proposed AI-powered approach. Chapter Three focuses on the research methodology employed in the development of the music recommendation system. The methodology includes data collection, preprocessing, feature extraction, algorithm selection, model training, and evaluation techniques. The chapter also discusses the dataset used, evaluation metrics, and experimental setup to validate the effectiveness of the AI-powered system in providing accurate and diverse music recommendations. Chapter Four presents the findings of the research, including the performance evaluation of the AI-powered music recommendation system in terms of recommendation accuracy, diversity, novelty, and user satisfaction. The results demonstrate the effectiveness of the system in improving music recommendation quality and user engagement compared to traditional approaches. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and proposing recommendations for future work. The study contributes to the field of music recommendation systems by introducing an innovative AI-powered approach that enhances user experience and engagement in music streaming platforms. The findings of this research can benefit music streaming services, content providers, and users by improving the quality and personalization of music recommendations. In conclusion, the development of an AI-powered music recommendation system represents a significant advancement in the field of music technology, offering a promising solution to the challenges of music discovery and user engagement in digital music platforms. The research findings underscore the potential of AI-driven technologies to revolutionize the music streaming industry and enhance user satisfaction.