  Numerous industries have benefited from the use of machine learning and
fashion in industry is no exception. By gaining a better understanding of what
makes a good outfit, companies can provide useful product recommendations to
their users. In this project, we follow two existing approaches that employ
graphs to represent outfits and use modified versions of the Graph neural
network (GNN) frameworks. Both Node-wise Graph Neural Network (NGNN) and
Hypergraph Neural Network aim to score a set of items according to the outfit
compatibility of items. The data used is the Polyvore Dataset which consists of
curated outfits with product images and text descriptions for each product in
an outfit. We recreate the analysis on a subset of this data and compare the
two existing models on their performance on two tasks Fill in the blank (FITB):
finding an item that completes an outfit, and Compatibility prediction:
estimating compatibility of different items grouped as an outfit. We can
replicate the results directionally and find that HGNN does have a slightly
better performance on both tasks. On top of replicating the results of the two
papers we also tried to use embeddings generated from a vision transformer and
witness enhanced prediction accuracy across the board
