This is a simple movie recommender system tailored to members of Letterboxd, a social network for cinema lovers. While the website is complete, it lacks an important feature, both with free and paid subscriptions: movie recommendation. This project aims to make up for it.
Sidenote: The official answer to this is here but it is not satisfactory either.
Download the dataset from here and unzip it. Move the csv files in a new folder called letterboxd_data
Install the requirements with
pip install -r requirements.txt
Export your letterboxd account data. Move the file ratings.csv in the folder user_data. Then launch reco_surprise.ipynb
While looking for datasets to achieve my needs, I came across this repository with the corresponding up-and-running website. This is actually very close to the end result I intended. However, just because someone else had the idea before does not mean I cannot do it myself! For this reason I did not look at his code and coded the thing from scratch. Another good motivation is that I wish additional features, such as filtering my recommendations by movie duration, importings filter settings, have more than 50 recommendation results, etc.
Several algorithms are proposed, from easiest to more advanced.
I suggest this great tutorial to get started with most simple recommender systems. Even the most basic system gives good results for a single user.
- updated database for movies released in 2023 or later
- accurate filters for movie results
- web app deployment with flask and heroku
- online hosting of database
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent