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Introduction

In recent years, e-commerce platforms have gained significant popularity among consumers, providing convenient ways to purchase products and services online. With the vast amount of options available on these platforms, users often find it overwhelming and challenging to navigate through the vast array of choices. This has led to the emergence of recommendation systems, which aim to personalize the shopping experience for users by suggesting products based on their preferences and browsing behavior.

Designing an effective recommendation system for e-commerce platforms is crucial in enhancing user experience, increasing sales, and fostering customer loyalty. This thesis aims to explore the design and implementation of a recommendation system for e-commerce platforms, with a focus on improving user satisfaction and engagement.

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 Introduction to Recommendation Systems
2.2 Types of Recommendation Systems
2.3 Collaborative Filtering
2.4 Content-Based Filtering
2.5 Hybrid Recommendation Systems
2.6 Personalization in E-commerce
2.7 Challenges in Designing Recommendation Systems
2.8 Evaluation Metrics for Recommendation Systems
2.9 Case Studies on Recommendation Systems
2.10 Future Trends in Recommendation Systems

Chapter 3: Research Methodology
3.1 Introduction
3.2 Research Design
3.3 Data Collection Methods
3.4 Data Preprocessing
3.5 Algorithm Selection
3.6 Model Training and Evaluation
3.7 Performance Metrics
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Analysis of Data
4.3 Evaluation of Recommendation System
4.4 Comparison with Existing Systems
4.5 User Feedback and Satisfaction
4.6 Limitations and Future Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Implications for Practice
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview

The rise of e-commerce platforms has revolutionized the way consumers shop, with the convenience of online shopping becoming increasingly popular. However, with the vast array of products available, users often find it overwhelming to navigate through the options. Recommendation systems have emerged as a solution to personalize the shopping experience for users, by suggesting products based on their preferences and behaviors.

This thesis aims to explore the design and implementation of a recommendation system for e-commerce platforms, with a focus on enhancing user experience and increasing sales. The literature review will provide an overview of recommendation systems, types of filtering techniques, challenges in designing recommendation systems, personalization in e-commerce, and evaluation metrics.

The research methodology will outline the research design, data collection methods, algorithm selection, model training and evaluation, performance metrics, and ethical considerations. The discussion of findings will analyze the data, evaluate the recommendation system, compare it with existing systems, and present user feedback and satisfaction.

In conclusion, this thesis will contribute to knowledge on designing recommendation systems for e-commerce platforms, with implications for practice and recommendations for future research. The goal is to provide a comprehensive understanding of how recommendation systems can enhance the shopping experience for users and ultimately drive sales on e-commerce platforms.

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