For this project, I built book recommender systems using collaborative filtering and singular value decomposition approach to help user discover new book that match their need.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
The code is written in Python 3.7 using Jupyter Notebook.
You need to install Surprise (a Python library for recommender system) in order to run the code.
Flask is used for development of backend API.
With pip (you'll need numpy, and a C compiler. Windows users might prefer using conda):
$ pip install numpy
$ pip install scikit-surprise
With conda:
$ conda install -c conda-forge scikit-surprise
I built a backend API and host it on Pythonanywhere. This is the website for demonstration of user-based collaborative filtering.
The website frontend is developed using React and collaborated with @ChloeLiang at this repository.