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Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username. A user's "star" ratings are scraped from their Letterboxd profile and assigned numerical ratings from 1 to 10 (accounting for half stars). Their ratings are then combined with a sample of ratings from the top 4000 most active 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 maximum sample size that a user is allowed to select is 500,000 samples, though there are over five million ratings in the full dataset from the top 4000 Letterboxd users alone.
Features
- Gather movie ratings from any Letterboxd user
- Provides movie recommendations based on ratings data from thousands of other users
- The more movies you've rated on Letterboxd, the better and more personalized the recommendations will be
- It can provide recommendations to any user
- 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 other users and movies
Project Samples
