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Table of Contents

Chapter One: Introduction
1.1 Background of the Study
1.2 Objectives of the Study
1.3 Limitations of the Study
1.4 Scope of the Study

Chapter Two: Literature Review
2.1 Overview of Recommender Systems
2.2 Types of Recommender Systems
2.3 Importance of Recommender Systems in Online Streaming Platforms
2.4 Challenges and Issues in Recommender Systems for Online Streaming Platforms

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques

Chapter Four: Discussion of Findings
4.1 Analysis of Data
4.2 Interpretation of Results
4.3 Implications of Findings for Online Streaming Platforms

Chapter Five: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Conclusion
5.3 Recommendations for Future Research

Brief Overview on Recommender Systems for Online Streaming Platforms:

Recommender systems are algorithms used by online streaming platforms to suggest content to users based on their preferences, browsing history, and behavior on the platform. These systems play a crucial role in enhancing user experience and increasing user engagement on the platform.

There are different types of recommender systems, including collaborative filtering, content-based filtering, and hybrid recommender systems. Collaborative filtering recommends content based on the preferences of similar users, while content-based filtering suggests content based on the characteristics of the items themselves. Hybrid recommender systems combine the strengths of both collaborative and content-based filtering to provide more accurate and personalized recommendations to users.

Despite the benefits of recommender systems, there are also challenges and issues that need to be addressed, such as cold start problem, data sparsity, and algorithm bias. Research on recommender systems for online streaming platforms aims to improve the accuracy and effectiveness of these systems, ultimately enhancing user satisfaction and retention on the platform.

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