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Introduction

In the age of digital music streaming, music recommendation engines have become an integral part of the user experience. These engines help users discover new music based on their preferences, listening history, and behavior. Collaborative filtering is a popular technique used in recommendation systems to provide personalized recommendations by analyzing user behavior and preferences.

This thesis focuses on developing a music recommendation engine using collaborative filtering. The goal of this research is to enhance the user experience by providing accurate and personalized music recommendations to users. By analyzing user data and behavior, the engine can recommend songs, artists, and playlists that align with the user’s taste and preferences.

Chapter 1: 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 Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of collaborative filtering
2.2 Music recommendation systems
2.3 Collaborative filtering algorithms
2.4 Evaluation metrics for recommendation systems
2.5 User modeling in recommendation systems
2.6 Cold start problem in recommendation systems
2.7 Hybrid recommendation systems
2.8 User interface design for recommendation systems
2.9 Case studies of existing music recommendation engines
2.10 Future trends in music recommendation systems

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 User profiling and modeling
3.3 Collaborative filtering algorithm selection
3.4 Evaluation methodology
3.5 User interface design
3.6 System architecture
3.7 Data storage and management
3.8 Testing and validation

Chapter 4: System Implementation
4.1 Software tools and technologies
4.2 System requirements
4.3 Data integration and processing
4.4 Algorithm implementation
4.5 User interface development
4.6 System testing and validation
4.7 Performance optimization
4.8 Scalability and deployment

Chapter 5: Conclusion and Summary
In conclusion, this thesis presents the development of a music recommendation engine using collaborative filtering. By leveraging user data and behavior, the engine can provide personalized music recommendations to users, enhancing their overall music streaming experience. The implementation of the system has shown promising results in terms of accuracy and user satisfaction. Future work includes further optimization of the algorithms, scalability improvements, and integration of additional features to enhance the recommendation engine’s capabilities.

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