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Introduction

Recommender Systems have become an essential part of our daily lives, aiding us in discovering new products, services, and content that align with our preferences and interests. In recent years, there has been a growing interest in incorporating recommender systems into social networks to enhance user experience and engagement. This thesis aims to explore the design and implementation of recommender systems in social networks to improve user satisfaction and interaction.

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 Introduction to Recommender Systems
2.2 Types of Recommender Systems
2.3 Collaborative Filtering
2.4 Content-based Filtering
2.5 Hybrid Recommender Systems
2.6 Recommender Systems in Social Networks
2.7 Challenges in Recommender Systems for Social Networks
2.8 Evaluation Metrics for Recommender Systems
2.9 User Modeling in Recommender Systems
2.10 Personalization in Recommender Systems

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Preprocessing Techniques
3.4 Algorithm Selection
3.5 Implementation of Recommender Systems
3.6 Evaluation Strategy
3.7 Ethical Considerations
3.8 Limitations of Research Methodology

Chapter Four: Discussion of Findings
4.1 Analysis of Data
4.2 Performance Evaluation of Recommender Systems
4.3 Comparison of Algorithms
4.4 User Feedback and Satisfaction
4.5 Challenges and Limitations
4.6 Recommendations for Future Research
4.7 Implications for Social Networks
4.8 Practical Applications

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Existing Literature
5.3 Implications for Practice
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview:

Recommender systems play a crucial role in enhancing user experience and engagement in social networks by providing personalized recommendations to users. This thesis delves into the design and implementation of recommender systems in social networks, addressing the challenges and exploring the potential benefits. Chapter One provides an introduction to the topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure, and defining key terms. Chapter Two conducts a comprehensive literature review on recommender systems, focusing on types, algorithms, challenges, evaluation metrics, user modeling, and personalization. Chapter Three details the research methodology, including design, data collection, preprocessing, algorithm selection, implementation, evaluation, and ethical considerations. Chapter Four discusses the findings from the analysis, performance evaluation, comparison of algorithms, user feedback, challenges, recommendations, and implications. Finally, Chapter Five offers a conclusion and summary of the project, highlighting the contributions, implications, future research directions, and overall conclusions on recommender systems for social networks.

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