{"repo":"sdl60660/letterboxd_recommendations","free":true,"listed":false,"github":"https://github.com/sdl60660/letterboxd_recommendations","clone":"git clone https://github.com/sdl60660/letterboxd_recommendations.git","description":"Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username","language":"Python","stars":405,"topics":["letterboxd-recommendations","letterboxd","movie-recommendations","svd","web-scraping","redis-queue","collaborative-filtering","flask"],"license":"GPL-3.0","category":"scrapers-browser-automation","readme_excerpt":"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 Sta","default_branch":null,"files":null,"tree":[],"storefront":"/r/sdl60660","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/sdl60660/letterboxd_recommendations/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}