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Introduction

In recent years, online grocery shopping has become increasingly popular among consumers due to its convenience and time-saving benefits. With the rise of e-commerce platforms, customers can now purchase their groceries from the comfort of their own homes and have them delivered directly to their doorstep. However, with the vast array of products available online, customers often face the challenge of finding the products that best meet their preferences and needs.

Recommender systems have emerged as a solution to help online shoppers navigate the plethora of products available and make more informed purchasing decisions. By analyzing customer preferences and purchase history, recommender systems can provide personalized recommendations that are tailored to the individual needs of each customer. This not only enhances the shopping experience for customers but also increases customer satisfaction and loyalty.

This thesis aims to explore the use of recommender systems for online grocery shopping using customer preferences and purchase history. By understanding how these systems work and how they can be optimized, we can improve the overall shopping experience for online grocery shoppers and increase sales for e-commerce platforms.

Table of Contents

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 Recommender Systems
2.2 Types of Recommender Systems
2.3 Collaborative Filtering
2.4 Content-Based Filtering
2.5 Hybrid Recommender Systems
2.6 Personalization in E-commerce
2.7 Customer Preferences and Purchase History
2.8 Case Studies on Recommender Systems in Online Grocery Shopping
2.9 Challenges and Opportunities in Recommender Systems
2.10 Summary of Literature Review

Chapter 3: Research Methodology

3.1 Introduction to Research Methodology
3.2 Research Design
3.3 Data Collection Methods
3.4 Data Analysis Methods
3.5 Sample Selection
3.6 Instrumentation
3.7 Data Validity and Reliability
3.8 Ethical Considerations
3.9 Limitations of Research Methodology

Chapter 4: Discussion of Findings

4.1 Analysis of Customer Preferences
4.2 Analysis of Purchase History
4.3 Performance Evaluation of Recommender Systems
4.4 Comparison of Different Recommender Systems
4.5 Recommendations for Improving Recommender Systems
4.6 Implications for Online Grocery Shopping Platforms
4.7 Future Research Directions
4.8 Summary of Findings

Chapter 5: Conclusion and Summary

5.1 Summary of Research Objectives
5.2 Conclusions
5.3 Contributions to Knowledge
5.4 Practical Implications
5.5 Limitations of Study
5.6 Recommendations for Future Research
5.7 Closing Remarks

Overall, this thesis will provide a comprehensive overview of recommender systems for online grocery shopping using customer preferences and purchase history. By examining the effectiveness of these systems and their impact on customer satisfaction and sales, we can better understand how to optimize the online shopping experience for consumers.

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