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Introduction

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the study
1.3 Problem statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of recommender systems
2.2 Traditional recommender systems
2.3 Explainable AI in recommender systems
2.4 Importance of explainability in recommender systems
2.5 Techniques for achieving explainability in recommender systems
2.6 Challenges in implementing explainable AI in recommender systems
2.7 Case studies of explainable AI in recommender systems
2.8 Future trends in explainable AI for recommender systems
2.9 Summary of the literature review

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data collection and preprocessing
3.3 Feature selection and engineering
3.4 Model selection and evaluation
3.5 Explainability techniques implementation
3.6 User interaction design
3.7 Performance evaluation metrics
3.8 Ethical considerations
3.9 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Technology stack used
4.2 System development process
4.3 Model training and deployment
4.4 Integration with existing recommender systems
4.5 User testing and feedback
4.6 System optimization and scalability
4.7 Results and findings
4.8 Comparison with traditional recommender systems
4.9 Challenges and lessons learned

Chapter 5: Conclusion and Summary
5.1 Summary of the study
5.2 Contributions and implications
5.3 Future research directions
5.4 Conclusion

Thesis Overview

The field of artificial intelligence (AI) has seen significant advancements in recent years, especially in the area of recommender systems. Recommender systems are widely used in various applications such as e-commerce, social media, and entertainment platforms to personalize user experiences by providing relevant recommendations. However, the lack of transparency and interpretability in traditional recommender systems has led to growing concerns about their trustworthiness and reliability.

Explainable AI (XAI) has emerged as a promising approach to address this issue by providing explanations for the recommendations made by AI systems. In this thesis, we focus on the application of XAI in recommender systems to enhance transparency, trust, and user satisfaction. The objective of this study is to design, implement, and evaluate an XAI-powered recommender system that not only provides accurate recommendations but also explains the reasoning behind them.

Chapter 1 provides an introduction to the research topic, including the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on traditional recommender systems, the importance of explainability, techniques for achieving explainability, challenges, case studies, and future trends in XAI for recommender systems.

Chapter 3 details the system design and methodology, including the architecture, data collection, feature engineering, model selection, explainability techniques, user interaction design, performance metrics, and ethical considerations. Chapter 4 describes the system implementation process, including the technology stack, development, training, deployment, integration, testing, optimization, results, comparisons, challenges, and lessons learned.

Chapter 5 concludes the thesis with a summary of the study, contributions, implications, future research directions, and final thoughts. This thesis contributes to the growing body of knowledge on XAI for recommender systems and provides practical insights for researchers, practitioners, and stakeholders interested in developing transparent and trustworthy AI systems.

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