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Introduction

In recent years, music streaming services have become increasingly popular, providing users with access to vast libraries of music at their fingertips. With such a vast amount of music available, it can be overwhelming for users to sift through and discover new music that aligns with their preferences. This is where recommender systems come into play. Recommender systems use algorithms to analyze user data and provide personalized music recommendations, helping users discover new music that they may enjoy.

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 Introduction to recommender systems
2.2 Music recommendation algorithms
2.3 Collaborative filtering
2.4 Content-based filtering
2.5 Hybrid recommender systems
2.6 Evaluation of recommender systems
2.7 User modeling in music recommendation
2.8 Context-aware music recommendation
2.9 Cold-start problem in music recommendation
2.10 Challenges and future directions in music recommendation

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Algorithm selection
3.5 Evaluation metrics
3.6 Experiment design
3.7 Performance evaluation
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of experimental results
4.2 Comparison of different recommendation algorithms
4.3 User feedback and satisfaction
4.4 Implementation challenges
4.5 Insights for future research
4.6 Implications for music streaming services

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Limitations of the study
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

Recommender Systems for Music Streaming

Music streaming services have revolutionized the way we listen to music, providing us with access to millions of songs from around the world. However, with such a vast amount of music available, it can be challenging for users to discover new music that aligns with their preferences. This is where recommender systems come into play. Recommender systems use algorithms to analyze user data and provide personalized music recommendations, helping users discover new music that they may enjoy.

This thesis explores the use of recommender systems in the context of music streaming services, with a focus on understanding different recommendation algorithms, evaluating their performance, and addressing the challenges and limitations of current systems. The study aims to contribute to the existing literature on music recommendation by providing insights into user preferences, exploring new approaches to music recommendation, and identifying opportunities for future research in the field.

Chapter 1 provides an introduction to recommender systems for music streaming, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review on music recommendation algorithms, user modeling, context-aware recommendation, and challenges in music recommendation. Chapter 3 outlines the research methodology, including research design, data collection, preprocessing, algorithm selection, evaluation metrics, experiment design, and performance evaluation.

Chapter 4 discusses the findings of the study, analyzing experimental results, comparing different recommendation algorithms, exploring user feedback, and identifying implementation challenges. Chapter 5 presents the conclusion and summary of the thesis, summarizing the findings, highlighting contributions to the field, discussing limitations, providing recommendations for future research, and concluding the study.

Overall, this thesis aims to contribute to the field of music recommendation by providing insights into the current state of the art, evaluating the performance of different recommendation algorithms, and identifying opportunities for future research in the field of recommender systems for music streaming.

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