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Introduction

In recent years, recommendation systems have become an integral part of many online platforms, helping users discover new movies, books, products, and more based on their preferences and past behavior. Machine learning algorithms have played a crucial role in developing these recommendation systems, as they can analyze vast amounts of data to make personalized suggestions to users. This research aims to develop a recommendation system using various machine learning algorithms to improve the accuracy and effectiveness of recommendations.

1.2 Background of Study

– Evolution of recommendation systems
– Importance of recommendation systems in e-commerce and online platforms
– Previous research on recommendation systems and machine learning algorithms

1.3 Problem Statement

Despite the advancements in recommendation systems, there are still challenges in accurately predicting user preferences and providing personalized recommendations. This research aims to address these challenges by developing a recommendation system using machine learning algorithms.

1.4 Objective of Study

– To develop a recommendation system using machine learning algorithms
– To improve the accuracy and effectiveness of recommendations
– To compare the performance of different machine learning algorithms in recommendation systems

1.5 Limitation of Study

– Limited availability of data for training and testing
– Complexity of implementing and optimizing machine learning algorithms
– Potential bias in recommendations based on user behavior

1.6 Scope of Study

This research will focus on developing a recommendation system for e-commerce platforms, specifically for recommending products to users based on their browsing and purchase history.

1.7 Significance of Study

The findings of this research can help improve the user experience on e-commerce platforms, increase customer satisfaction, and ultimately drive sales by providing personalized recommendations to users.

1.8 Structure of the Thesis

– Chapter 1: Introduction
– Chapter 2: Literature Review
– Chapter 3: System Design and Methodology
– Chapter 4: System Implementation
– Chapter 5: Conclusion and Summary

1.9 Definition of Terms

– Recommendation system: A system that provides personalized suggestions to users based on their preferences and past behavior
– Machine learning algorithms: Algorithms that enable computers to learn from data and make predictions without being explicitly programmed

Thesis Overview:

The thesis on “Developing a recommendation system using machine learning algorithms” aims to explore the potential of machine learning algorithms in improving the accuracy and effectiveness of recommendation systems. The research will focus on developing a recommendation system for e-commerce platforms, with the goal of providing personalized suggestions to users based on their browsing and purchase history.

Chapter 1 will provide an introduction to the research topic, including background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms.

Chapter 2 will present a comprehensive review of the existing literature on recommendation systems and machine learning algorithms, highlighting the evolution of recommendation systems, their importance in e-commerce, and previous research in the field.

Chapter 3 will detail the system design and methodology, including the selection of machine learning algorithms, data collection and preprocessing methods, model training, and evaluation techniques.

Chapter 4 will focus on the implementation of the recommendation system, detailing the steps taken to develop and optimize the system for accurate and personalized recommendations.

Chapter 5 will provide a conclusion and summary of the research findings, discussing the implications of the study, its contributions to the field, and potential areas for future research.

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