Letterboxd Recommendation App 🎥

https://syzygy-lttrbxd-movie-recc.netlify.app/

The Letterboxd Recommendation App is a web application that provides personalized movie recommendations based on user ratings from Letterboxd. By leveraging collaborative filtering techniques, the app suggests films that align with a user's unique preferences.

Features

• Personalized Recommendations: Delivers movie suggestions tailored to individual user tastes.
• Collaborative Filtering: Employs Singular Value Decomposition (SVD) to analyze user rating patterns and predict preferences.
• Web Interface: Offers an intuitive interface for users to input their Letterboxd username and receive recommendations.

Technologies Used

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• Machine Learning: Surprise library for building the recommendation model
• Data Handling: Pandas for data manipulation
• Web Scraping: Beautiful Soup and Requests for extracting user ratings from Letterboxd

Setup Instructions

1. Clone the Repository

git clone https://github.com/Syzygyastro/letterboxd_recommendation_app.git

2. Navigate to the Project Directory

cd letterboxd_recommendation_app

3. Set Up a Virtual Environment

python -m venv env source env/bin/activate # On Windows: env\Scripts\activate

4. Install Dependencies

pip install -r requirements.txt

5. Train the Recommendation Model

Ensure you have a dataset of user ratings in CSV format. The provided script train_svd.py can be used to train the model. python train_svd.py

This will generate a svd_model.pkl file containing the trained model.

6. Run the Application

flask run

Access the app at http://localhost:5000

Note

Ensure your Letterboxd ratings are publicly accessible for the app to retrieve and analyze your data effectively.