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Introduction

Graph neural networks have gained significant attention in recent years for their ability to effectively model relationships and dependencies among entities in complex relational data. In the context of recommender systems, where the goal is to suggest items or users to users based on their preferences and behavior, graph neural networks have shown promise in capturing intricate patterns and interactions in the user-item graph to enhance recommendation accuracy. This thesis explores the application of graph neural networks in recommender systems and aims to investigate their effectiveness in improving recommendation quality.

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 Traditional Recommender Systems
2.3 Graph-based Recommender Systems
2.4 Neural Networks in Recommender Systems
2.5 Graph Neural Networks
2.6 Applications of Graph Neural Networks in Recommender Systems
2.7 Challenges and Limitations of Graph Neural Networks
2.8 Evaluation Metrics for Recommender Systems
2.9 Comparative Analysis of Recommender Systems
2.10 Research Gaps and Future Directions

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Graph Construction
3.5 Model Architecture
3.6 Training and Evaluation
3.7 Experimental Setup
3.8 Performance Metrics
3.9 Statistical Analysis

Chapter Four: Discussion of Findings
4.1 Performance Comparison of Graph Neural Networks and Traditional Recommender Systems
4.2 Impact of Graph Construction Methods on Recommendation Quality
4.3 Effectiveness of Different Graph Neural Network Architectures
4.4 Interpretability of Graph Neural Networks in Recommender Systems
4.5 Scalability and Efficiency of Graph Neural Networks
4.6 Robustness and Generalization of Graph Neural Networks
4.7 Comparison with State-of-the-Art Recommender Systems
4.8 Insights from Experimental Results

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Limitations and Future Research Directions
5.5 Conclusion

Thesis Overview on Graph Neural Networks for Recommender Systems:

Graph neural networks have emerged as a powerful tool for modeling complex relationships and dependencies in relational data, making them well-suited for applications in recommender systems. In this thesis, we investigate the effectiveness of graph neural networks in improving recommendation quality by capturing intricate patterns and interactions in the user-item graph. The research is structured into five chapters, each addressing different aspects of the study.

Chapter one provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. Chapter two presents a comprehensive literature review on traditional recommender systems, graph-based recommender systems, neural networks, graph neural networks, applications, challenges, evaluation metrics, comparative analysis, research gaps, and future directions in the field.

Chapter three details the research methodology, including design, data collection, preprocessing, graph construction, model architecture, training, evaluation, experimental setup, performance metrics, and statistical analysis. Chapter four discusses the findings from experiments, including performance comparison, impact of graph construction methods, effectiveness of different architectures, interpretability, scalability, efficiency, robustness, and generalization of graph neural networks.

Finally, chapter five offers a conclusion and summary of the study, highlighting key findings, contributions, implications for practice, limitations, and future research directions. Overall, this thesis aims to shed light on the potential of graph neural networks in enhancing recommender systems and providing valuable insights for further research in the field.

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