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Introduction

In recent years, recommender systems have become an integral part of many online platforms, enabling users to discover new content and products tailored to their preferences. However, traditional recommender systems often lack transparency and interpretability, leaving users uncertain about how recommendations are generated. This has led to a growing interest in developing explainable recommender systems that can provide users with explanations for why certain items are recommended to them.

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 Overview of recommender systems
2.2 Traditional recommender systems
2.3 Explainable recommender systems
2.4 Importance of explainability in recommender systems
2.5 Methods for generating explanations in recommender systems
2.6 Evaluation of explainable recommender systems
2.7 User perception of explainable recommender systems
2.8 Challenges in developing explainable recommender systems
2.9 Case studies of explainable recommender systems
2.10 Future directions in the field of explainable recommender systems

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Model selection
3.5 Implementation of explainable recommender system
3.6 Evaluation metrics
3.7 User studies
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Performance comparison of explainable and non-explainable recommender systems
4.2 Analysis of user feedback on explanations
4.3 Impact of explanations on user trust and satisfaction
4.4 Interpretability of different explanation methods
4.5 Comparison of different explanation generation techniques
4.6 Practical implications for businesses implementing explainable recommender systems
4.7 Limitations of the study
4.8 Future research directions

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

Thesis Overview

Explainable recommender systems have gained significant attention in recent years due to the need for transparency and interpretability in recommendation algorithms. This thesis aims to investigate the importance of explainability in recommender systems and evaluate different methods for generating explanations.

Chapter 1 provides an introduction to the topic, including the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on recommender systems, explainable recommender systems, methods for generating explanations, evaluation metrics, user perception, challenges, and case studies.

Chapter 3 outlines the research methodology, including research design, data collection, preprocessing, model selection, implementation of an explainable recommender system, evaluation metrics, user studies, and ethical considerations. Chapter 4 discusses the findings of the study, including the performance comparison of explainable and non-explainable recommender systems, user feedback on explanations, impact on user trust and satisfaction, interpretability of different explanation methods, comparison of explanation generation techniques, practical implications, limitations, and future research directions.

Chapter 5 concludes the thesis with a summary of findings, contributions, practical implications, recommendations for future research, and a conclusion. Through this thesis, we aim to contribute to the growing body of research on explainable recommender systems and provide insights for businesses looking to implement transparent and interpretable recommendation algorithms.

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