Movie Recommendation System

Project developed by Fiona Verzivolli, Andrew Sasmito, Saahil Kapasi, and Naoroj Farhan.

This project implements a movie recommendation system that leverages Louvain's Algorithm for graph community detection to provide personalized movie suggestions based on user preferences and interactions.

Key Features

• Advanced Recommendation Engine: Utilizes Louvain's Algorithm, a state-of-the-art method for detecting communities within large networks, to identify groups of similar movies. This enhances the accuracy and relevance of recommendations.

• Core Technologies:

  • Python: The backbone of the system, Python is used for all core logic and computational tasks, ensuring robust performance and easy integration with other tools.
  • NetworkX: A powerful library for the creation, manipulation, and study of complex networks. It is used here to handle graph data structures and perform clustering operations essential for the recommendation process.
  • Tkinter: Provides a user-friendly graphical interface that allows users to interact with the recommendation system easily. The GUI includes features like movie search, viewing recommendations, and user preference input.

• User Interaction:

  • Preference-Based Recommendations: Users can input their movie preferences and past interactions, which the system uses to tailor its recommendations.
  • Dynamic Updates: The recommendation engine continuously updates its suggestions based on new user data, ensuring that recommendations remain relevant over time.

Installation and Usage

1. Clone the Repository:

   git clone https://github.com/yourusername/movierecommendation.git
   cd movierecommendation

2. Install Dependencies: Make sure you have Python installed, then run:

   pip install -r requirements.txt

3. Run the Application:

   python app.py

4. Interact with the GUI:

   • Use the provided Tkinter interface to input your movie preferences.
   • View the recommended movies based on your input and interactions.

Technical Details

• Graph Community Detection: The Louvain Algorithm is a heuristic method that is efficient in detecting communities in large networks, making it ideal for this recommendation system. The algorithm works by optimizing the modularity of a partition of the network.
• Graph Representation: Movies and user interactions are represented as nodes and edges in a graph. This structure allows for the identification of clusters of similar movies.
• User-Friendly Interface: The Tkinter-based GUI is designed to be intuitive, ensuring that users of all technical levels can easily navigate and utilize the system.

Contributions

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or bug reports.

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