Project Abstract

Abstract
The music industry has witnessed a significant transformation in recent years due to advancements in technology. Artificial Intelligence (AI) has played a crucial role in revolutionizing the way music is created, distributed, and consumed. This research project focuses on the development of an AI-powered Music Recommendation System, which aims to enhance the music listening experience for users by providing personalized recommendations based on their preferences. Chapter One Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objective of Study 1.5 Limitation of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Research 1.9 Definition of Terms Chapter Two Literature Review 2.1 Evolution of Music Recommendation Systems 2.2 Role of AI in Music Industry 2.3 User Preferences in Music Recommendation 2.4 Collaborative Filtering Algorithms 2.5 Content-Based Filtering Methods 2.6 Hybrid Recommendation Systems 2.7 Evaluation Metrics for Recommendation Systems 2.8 Challenges in Music Recommendation Systems 2.9 Current Trends in AI-powered Music Recommendation Chapter Three Research Methodology 3.1 Research Design 3.2 Data Collection Methods 3.3 Data Preprocessing Techniques 3.4 Feature Selection and Engineering 3.5 Algorithm Selection and Implementation 3.6 Evaluation Strategy 3.7 Performance Metrics 3.8 Ethical Considerations in AI-powered Systems Chapter Four Discussion of Findings 4.1 System Architecture 4.2 Data Processing Workflow 4.3 User Interface Design 4.4 Recommendation Algorithms 4.5 Performance Evaluation Results 4.6 User Feedback and Validation 4.7 Comparison with Existing Systems Chapter Five Conclusion and Summary The Development of an AI-powered Music Recommendation System has the potential to revolutionize the way users discover and enjoy music. By leveraging AI algorithms and user preferences, the system can provide personalized recommendations that cater to individual tastes and preferences. The research findings highlight the effectiveness of the system in enhancing the music listening experience for users. Future research directions include exploring new AI techniques and improving the scalability and efficiency of the recommendation system. Keywords Artificial Intelligence, Music Recommendation System, Personalization, User Preferences, Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation, Evaluation Metrics.

Project Overview