Fashion Recommendation: Outfit Compatibility using GNN

Numerous industries have benefited from the use of machine learning, and the fashion 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. 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 - (1) Fill-in-the-blank (FITB): finding an item that completes an outfit, and (2) Compatibility prediction: estimating compatibility of different items grouped as an outfit. We are able to replicate the results directionally and find that HGNN does have a slightly better performance on both tasks.