A good recipe recommendation system that takes what a user is familiar with and suggests new recipes a few degrees removed can encourage people to try new things without overwhelming them with something new. This app utilizes the existing data for users, and builds a recipe network graph of recomendations for each user using Collaborative filtering and the similar recipes for each recipe using the Content Based Filtering.
- Node.js
- Yarn
- VSCode
- (Optional) Vue CLI
- A web browser (Internet Explorer not supported)
- Python 3
- Python dependencies: pip, pandas, numpy, scikit-learn
Please install python-pip and the following dependencies using pip. For RedHat Linux based systems use the commands below:
RedHat Linux: sudo yum -y install python-pip
Unix / Ubuntu / Kali : sudo apt install python3-pip
Windows: python get-pip.py (Make sure you have python installed on pc and added to path variable)
pip3 install numpy
pip3 install pandas
pip3 install -U scikit-learn
Be sure to install all workspace recommended VSCode extensions that are listed in ./CSE6242Project/.vscode/extenstions.json.
VSCode has been configured via the workspace settings to autoformat code on file save by using the ESLint, Prettier, and Vetur extensions. This feature should become available one all workspace recommened extensions have been installed and VScode has been restarted.
Node.js is a dependency for our JS package manager, Yarn. It is also used to spin up our development web server.
Once Node is installed on your system, run the following commands:
node -v
npm -v
npm install --global yarn
yarn --version
Once the commands have been successfully executed, navigate to the goldberg root project directory where you should find a package.json file.
This command is used to build the project dependecies. Version control is managed by package.json and yarn.lock. Dependences live in the node_modules file.
yarn install
This command is used to run the frontend application only in a development environment via localhost.
yarn serve
To run the backend and the frontend application simultaneously, run
yarn dev
This will run the backend api at localhost:80 and the frontend api at localhost:8080
yarn build
yarn test
yarn test:e2e
yarn lint
node server.js
yarn dev