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Introduction

In recent years, the explosion of digital content and the rise of online streaming platforms have led to an overwhelming amount of choices for consumers. This has created the need for recommendation systems to help users discover new content that aligns with their interests. Among the various types of recommendation systems, content-based systems have gained popularity for their ability to suggest items based on the attributes of the items themselves.

This thesis focuses on building a content-based recommendation system for movies. The system will analyze the content of movies, such as genre, actors, directors, and plot keywords, to generate personalized recommendations for users. By leveraging machine learning algorithms, the system aims to improve the accuracy and relevance of movie recommendations for users.

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 Recommendation Systems
2.2 Types of Recommendation Systems
2.3 Content-based Filtering
2.4 Collaborative Filtering
2.5 Hybrid Recommendation Systems
2.6 Movie Recommendation Systems
2.7 Evaluation Metrics for Recommendation Systems
2.8 Machine Learning Algorithms for Recommendation Systems
2.9 Challenges in Building Recommendation Systems
2.10 Related Works in Content-based Movie Recommendation

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Feature Extraction
3.4 Similarity Calculation
3.5 Recommendation Generation
3.6 Model Evaluation
3.7 User Interface Design
3.8 Experimentation Plan

Chapter 4: System Implementation
4.1 Development Environment
4.2 Data Collection and Preparation
4.3 Feature Engineering
4.4 Model Training and Evaluation
4.5 Integration of Recommendation System
4.6 Testing and Validation
4.7 System Optimization
4.8 Deployment

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications of the Study
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview

Building a content-based recommendation system for movies is a challenging yet essential task in the era of digital content consumption. This thesis aims to address the growing need for personalized movie recommendations by leveraging the content of movies to generate accurate and relevant suggestions for users.

The study begins with an introduction to the research topic, providing background information on the importance of recommendation systems in the context of movie consumption. The problem statement highlights the need for content-based recommendation systems, while the objectives of the study outline the goals and expected outcomes of the research. The limitations and scope of the study set boundaries for the research, while the significance of the study emphasizes the potential impact of the proposed recommendation system.

The structure of the thesis is then outlined, providing a roadmap for the reader to navigate the contents of the research. Definitions of key terms are provided to ensure a common understanding of the concepts discussed throughout the thesis.

The literature review delves into the existing research on recommendation systems, focusing on content-based filtering, collaborative filtering, and hybrid systems. It also discusses movie recommendation systems, evaluation metrics, machine learning algorithms, and challenges in building recommendation systems. Related works in content-based movie recommendation are reviewed to provide context for the study.

The system design and methodology chapter details the architecture of the proposed recommendation system, including data collection, preprocessing, feature extraction, similarity calculation, recommendation generation, model evaluation, and user interface design. An experimentation plan is also outlined to test the effectiveness of the system.

The system implementation chapter describes the development environment, data collection, feature engineering, model training, integration, testing, optimization, and deployment of the recommendation system.

Finally, the conclusion and summary chapter summarizes the findings of the research, discusses the contributions and implications of the study, suggests future research directions, and concludes the thesis.

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