Abstract:
With the vast amount of movies available today, it can be overwhelming for users to find movies that align with their preferences. This project aims to develop a recommendation system that provides personalized movie recommendations to users based on their viewing history and preferences. By leveraging machine learning algorithms and collaborative filtering techniques, the proposed system will analyze user data, identify similar users with similar movie preferences, and generate personalized recommendations. The system’s accuracy and effectiveness will be evaluated using a dataset of movie ratings, and its potential for integration into existing movie streaming platforms will be explored.

Table of Contents:
Chapter 1: Introduction
1.1 Background and Motivation
1.2 Problem Statement
1.3 Objectives
1.4 Scope and Limitations
1.5 Methodology

Chapter 2: Literature Review
2.1 Overview of Recommendation Systems
2.2 Collaborative Filtering Techniques
2.3 Content-Based Filtering Techniques
2.4 Hybrid Recommendation Approaches
2.5 Summary of Existing Research

Chapter 3: Data Collection and Preprocessing
3.1 Movie Dataset Acquisition
3.2 Data Cleaning and Transformation
3.3 User Profiling and Preference Extraction
3.4 Data Splitting and Cross-Validation

Chapter 4: Recommendation Model Development
4.1 Collaborative Filtering Algorithms
4.2 Content-Based Filtering Algorithms
4.3 Hybrid Recommendation Model Design
4.4 Model Training and Optimization
4.5 Performance Evaluation and Validation

Chapter 5: System Implementation and Evaluation
5.1 System Architecture and Integration
5.2 User Interface Design
5.3 Personalized Movie Recommendations Generation
5.4 Performance Evaluation and Comparison
5.5 User Satisfaction and Feedback Analysis

5.6 Conclusion and Future Work