Movie Recommender is a web application that computes movie recommendations based on user input. The user provides three movie examples through an autocomplete text input component. The title, rating, and movie poster of the selected examples are displayed on the page. Then, the user clicks the Recommend button and receives recommendations according to content-based filtering of the TMBD 5000 Movie Dataset. The default similarity metric is cosine similarity, but other metrics are selectable in the backend.
The project contains two major sections: /frontend and /backend. Each directory contains an additional README.md outlining design choices in more detail.
The project uses React with TypeScript in the frontend and Flask with Python in the backend.
It is necessarily to configure the /frontend and /backend folders initially. See the respective README.mds for more information.
In order to run the frontend, cd into /frontend and run
npm startIn order to run the backend, cd into the /backend and run
python3 -m src