Thesis Abstract

Abstract
The continuous growth of digital music consumption has led to an overwhelming amount of music content available to users. In this context, music recommender systems play a crucial role in assisting users to discover new music based on their preferences. This research project focuses on the development of a music recommender system using machine learning algorithms to enhance the music discovery experience for users. The study aims to address the challenge of information overload in the music domain by providing personalized recommendations to users. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of music recommender systems and the motivation behind this research project. Chapter 2 presents a comprehensive literature review covering ten key aspects related to music recommender systems, machine learning algorithms, user preferences, collaborative filtering techniques, content-based filtering methods, hybrid recommendation approaches, evaluation metrics, challenges in music recommendation, and existing research gaps. This chapter synthesizes existing knowledge and provides a theoretical framework for the development of the proposed music recommender system. Chapter 3 outlines the research methodology employed in this study, detailing the research design, data collection methods, dataset selection, preprocessing techniques, feature extraction, algorithm selection, model training, evaluation procedures, and performance metrics. The chapter elucidates the systematic approach followed to design and implement the music recommender system using machine learning algorithms. Chapter 4 delves into the detailed discussion of the findings obtained from the implementation of the music recommender system. The chapter analyzes the performance of different machine learning algorithms, evaluates the effectiveness of the recommendation system in generating personalized music suggestions, discusses the impact of user feedback on the recommendation quality, and explores the implications of the results on enhancing user experience in music discovery. Chapter 5 concludes the thesis by summarizing the key findings, discussing the contributions of the study, reflecting on the limitations encountered during the research process, suggesting future research directions, and emphasizing the practical implications of the developed music recommender system. The chapter encapsulates the significance of the project in advancing the field of music recommendation through the application of machine learning techniques. In conclusion, the "Development of a Music Recommender System Using Machine Learning Algorithms" thesis contributes to the academic discourse on personalized music recommendation systems by demonstrating the feasibility and effectiveness of utilizing machine learning algorithms to enhance user experience in music discovery. The research findings provide valuable insights for researchers, practitioners, and music enthusiasts interested in leveraging technology to optimize music recommendations and cater to diverse user preferences in the digital music landscape.

Thesis Overview