SynthGAN is a GAN based model whose objective is to create synthetic collaborative datasets.
At present it consists of three components:
- Deep matrix factorization that is used to perform dimension reduction and generate a new dataset of emebeddings.
- Generative Adversarial Network is intended to be a fully modular component that can be built using just two build functions; one each for the generator and discriminator components.
- K-means Clustering is used to group together similar user, item data points.
This project is inspired by the following papers: