Developing an AI-powered Music Recommendation System for Personalized Listening Experiences

Thesis Abstract

Abstract
The rapid advancement of artificial intelligence (AI) technology has revolutionized various aspects of our daily lives, including the way we interact with and consume music. This thesis presents a comprehensive study on the development of an AI-powered music recommendation system designed to enhance personalized listening experiences for users. The primary objective of this research is to leverage AI algorithms to analyze user preferences, behaviors, and feedback in order to generate tailored music recommendations that cater to individual tastes and preferences. The study begins with an in-depth exploration of the background and context of AI in the music industry, highlighting the significant impact that AI-powered recommendation systems have had on music consumption patterns. By addressing the existing problem of generic music recommendations that often fail to resonate with individual users, this research aims to provide a solution that enhances user satisfaction and engagement. Through a detailed examination of the objectives of the study, the limitations and scope of the research, and the significance of the proposed AI-powered music recommendation system, this thesis sets the foundation for a rigorous investigation into the development and implementation of the system. The structure of the thesis is outlined to provide a roadmap for the subsequent chapters, which include a comprehensive review of relevant literature, a detailed analysis of the research methodology employed, a discussion of the findings, and a concluding summary. The literature review chapter delves into existing research and technologies related to music recommendation systems, AI algorithms, user modeling, and personalized content delivery. By synthesizing key insights from previous studies, this chapter establishes a theoretical framework that informs the design and implementation of the AI-powered music recommendation system. The research methodology chapter outlines the approach taken to collect, process, and analyze data for the development of the recommendation system. Key components such as data collection methods, algorithm selection, system design, and evaluation metrics are discussed in detail to provide transparency and reproducibility in the research process. The discussion of findings chapter presents the results of the study, including the performance evaluation of the AI-powered music recommendation system, user feedback, and implications for future research and development. By critically analyzing the outcomes of the research, this chapter offers insights into the effectiveness and usability of the system in enhancing personalized listening experiences for users. In conclusion, this thesis summarizes the key findings, contributions, and implications of the research, emphasizing the potential of AI-powered music recommendation systems to transform the way users discover and engage with music. Through the development of a personalized recommendation system that adapts to individual preferences and behaviors, this research aims to enhance user satisfaction, promote music discovery, and foster a deeper connection between users and music content. Keywords Artificial Intelligence, Music Recommendation System, Personalization, User Modeling, Data Analysis, User Engagement, Music Consumption Patterns.