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Introduction

Recommender systems have become an essential tool for music streaming services in recent years, as they help users discover new music based on their preferences and behavior. One popular method for building recommender systems is matrix factorization, which involves breaking down a matrix into simpler components to better understand its structure. This thesis aims to explore the use of matrix factorization in recommending music to users on streaming platforms.

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 Two: Literature Review

2.1 Overview of Recommender Systems

2.2 Matrix Factorization Techniques

2.3 Music Recommender Systems

2.4 Collaborative Filtering

2.5 Content-based Filtering

2.6 Hybrid Recommender Systems

2.7 Evaluation Metrics for Recommender Systems

2.8 Cold Start Problem in Recommender Systems

2.9 User Preference Modeling

2.10 Music Dataset and Data Preprocessing

Chapter Three: Research Methodology

3.1 Research Design

3.2 Data Collection

3.3 Data Analysis Techniques

3.4 Experimental Setup

3.5 Evaluation Criteria

3.6 Implementation Details

3.7 Model Training and Testing

3.8 Performance Evaluation

Chapter Four: Discussion of Findings

4.1 Analysis of Recommender System Performance

4.2 Comparison of Matrix Factorization Techniques

4.3 Impact of User Preferences on Recommendations

4.4 User Satisfaction and Engagement

4.5 Recommendations for Music Streaming Services

4.6 Future Research Directions

4.7 Challenges and Limitations

4.8 Ethical Considerations

Chapter Five: Conclusion and Summary

5.1 Summary of Research Findings

5.2 Contributions to the Field

5.3 Implications for Music Streaming Services

5.4 Conclusion and Recommendations

Thesis Overview

Recommender systems have played a crucial role in enhancing user experience on music streaming services by providing personalized music recommendations based on user preferences and behavior. In this thesis, we will focus on the use of matrix factorization techniques to build an effective recommender system for music streaming platforms. The research will begin with a comprehensive literature review on recommender systems, matrix factorization, music recommendation techniques, and evaluation metrics. The methodology section will outline the research design, data collection, analysis techniques, experimental setup, and performance evaluation criteria. The discussion of findings will analyze the performance of the recommender system, compare different matrix factorization techniques, evaluate user satisfaction, and provide recommendations for music streaming services. The conclusion will summarize the research findings, discuss the contributions to the field, suggest future research directions, and highlight the implications for music streaming services. By exploring the potential of matrix factorization in music recommendation systems, this thesis aims to contribute to the advancement of personalized music discovery for users.

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