Letterboxd Recommendations
This project scrapes publicly-accessible Letterboxd data and creates a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username.
Live project: https://letterboxd.samlearner.com
Methodology
A user's "star" ratings are scraped their Letterboxd profile and assigned numerical ratings from 1 to 10 (accounting for half stars). Their ratings are then folded into a model that's been trained on a sample of ratings from other users on the site to create a collaborative filtering recommender model using singular value decomposition (SVD). All movies in the full dataset that the user has not rated are run through the model for predicted scores and the items with the top predicted scores are returned. Due to constraints in time and computing power, the maxiumum sample size that a user is allowed to select contains about five million ratings, though there are over 50 million ratings in the full dataset.
Notes
The underlying model is completely blind to genres, themes, directors, cast, or any other content information; it recommends only based on similarities in rating patterns between users and movies. I've found that it tends to recommend very popular movies often, regardless of an individual user's taste ("basically everyone who watches 12 Angry Men seems to like it, so why wouldn't you?"). I've also found that it occasionally just completely whiffs (I guess most people who watch "Taylor Swift: Reputation Stadium Tour" do like it, but it's not really my thing). I think that's just the nature of the beast, to some extent, particularly when working with a relatively small sample. It'll return 50 recommendations for a set of filters and that's usually enough to work with if I'm looking for something to watch, even if there are a couple misses here or there.
Getting started
To run the full stack locally (backend, crawlers, frontend), you’ll need Docker and Node installed.
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Create your environment file
cp .env.example .env
- Update any values as needed.
CONNECTION_URL (and MONGODB_URI) should point to your MongoDB instance. It defaults to the local Docker Mongo container (mongodb://mongo:27017).- If you have a TMDB API key, add it under
TMDB_KEY (optional but recommended for richer movie data).
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Run the setup script
This installs Python dependencies (via Pipenv), frontend dependencies (via npm), installs pre-commit hooks, builds Docker images, starts Mongo + Redis, creates indexes, seeds minimal data, and runs quick tests:
make bootstrap