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Movie Recommendation System

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Authors:Eugene Kuloba

Table of Contents

Project Overview

The Movie Recommendation System is a machine learning-powered application designed to enhance user engagement and satisfaction on our platform by providing personalized movie recommendations. By leveraging user behavior data and movie metadata, this system generates tailored movie suggestions, ultimately leading to increased user retention, content consumption, and user satisfaction.

Objectives

  1. Enhance User Experience: Deliver relevant and enjoyable movie recommendations to each user, improving their experience on our platform.

  2. Increase Content Consumption: Boost the consumption of movies available on our platform by guiding users toward content they are likely to enjoy.

Key Features

  1. User Personalization: Recommendations are customized based on individual user preferences and viewing history.

  2. Scalability: The system is designed to handle a growing user base and a vast catalog of movies.

  3. Recommendation Quality: Balancing relevance and diversity to provide high-quality recommendations.

  4. Data Privacy: Ensuring user data is handled securely and in compliance with privacy regulations.

Getting Started

Prerequisites

Before running the Movie Recommendation System, ensure you have the following prerequisites installed:

  1. Python (>=3.6)
  2. Apache Spark
  3. PySpark
  4. Jupyter Notebook (for data exploration and experimentation)

Installation

  1. Clone this repository:
git clone https://github.com/your/repo.git
  1. Navigate to the project directory:
cd movie-recommendation-system
  1. Set up your Python environment and install dependencies:
pip install -r requirements.txt
  1. Download the dataset and place it in the appropriate directory (data/) following the provided structure.

Usage

Data Preparation

  1. Data Collection: Gather user behavior data, movie metadata, and ratings data. Ensure data is appropriately cleaned and structured.

  2. Data Preprocessing: Use Jupyter Notebook or your preferred data analysis tool to preprocess the data, including handling missing values, feature engineering, and data transformations.

Model Training

  1. Model Development: Build machine learning models to predict user preferences based on historical data. Explore collaborative filtering, matrix factorization, or deep learning models for recommendation.

  2. Model Evaluation: Evaluate model performance using appropriate metrics (e.g., RMSE, precision-recall, AUC). Optimize model hyperparameters as needed.

Generating Recommendations

  1. Deployment: Integrate the recommendation system into your platform. Ensure scalability and real-time recommendation generation.

  2. User Feedback: Collect user feedback to fine-tune recommendations and assess user satisfaction.

Repository Structure

├── .gitignore
├── README.md
├── e
├── index.ipynb
├── requirements.txt
└──

Contributing

Contributions to the Movie Recommendation System project are welcome! Feel free to open issues, submit pull requests, or provide feedback.

License

This project is licensed under the MIT License.