Movie Recommender System

This is a movie recommender system built using Python, the MovieLens dataset, and Streamlit.

Setup

Docker

You can run the application using Docker. The Docker image can be pulled from Docker Hub.

1. Pull the Docker image:

   docker pull mlchals/recommender-app:latest
   

2. Run the Docker container:

   docker run -dp 8501:8501 mlchals/recommender-app:latest
   

   Note: The default port for Streamlit is 8501. Ensure that this port is free on your system.

Github

1. Clone the repository:

   git clone https://github.com/MlCHAL-S/recommender.git folder_name
   cd folder_name

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment: (On Windows):

   venv\Scripts\activate

4. Install the dependencies:

   pip install -r requirements.txt

5. Run the Streamlit app:

   streamlit run app.py

Project Structure

• main_folder/
  • recommender.py: Contains the Recommender class and related functions.
  • requirements.txt: List of required Python packages.
  • app.py: Contains the Streamlit app for the movie recommender system.
  • data/: Directory containing the datasets.
    • movies.csv: Dataset with movie information.
    • ratings.csv: Dataset with user ratings for movies.
  • README.md: Project documentation and setup instructions.

How It Works

• Collaborative Filtering: The system uses collaborative filtering to identify patterns in user behavior and suggest movies based on these patterns.
• k-Nearest Neighbors (k-NN): The k-NN algorithm is used to find similar movies based on user ratings.
• MovieLens Dataset: Recommendations are based on the MovieLens dataset, which includes a large number of movie ratings and metadata.
• Streamlit: The web application is built using Streamlit, providing a simple and interactive interface for users.

Usage

1. Launch the Streamlit app:

   streamlit run app.py

2. Navigate to the app in your browser (usually at http://localhost:8501).

3. Interact with the app to get movie recommendations. Start by selecting a movie you like from the dropdown menu to see similar movies.

Here is an example of how to interact with the recommender system:

1. Home Page: The home page provides an overview of the recommender system.

2. Recommendations Page: Select a movie from the dropdown menu

and get recommendations for similar movies.

Acknowledgements

• The MovieLens dataset is provided by the GroupLens research group.
• Streamlit is an open-source app framework for Machine Learning and Data Science projects.