# Fashion Recommendation: Outfit Compatibility using GNN

arXiv: https://arxiv.org/abs/2404.18040 · HTML: https://mlsanity.com/paper/2404.18040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Learning Fashion Compatibility with Bidirectional LSTMs](https://mlsanity.com/paper/1707.05691/) (2017)
- [Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences](https://mlsanity.com/paper/1509.07473/) (2015)
- [Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks](https://mlsanity.com/paper/1902.08009/) (2019)
- [Compatibility Family Learning for Item Recommendation and Generation](https://mlsanity.com/paper/1712.01262/) (2017)

## Built on by (most cited first, 1 of 1)

- [Hybrid-Hierarchical Fashion Graph Attention Network for Compatibility-Oriented and Personalized Outfit Recommendation](https://mlsanity.com/paper/2508.11105/) (2025)

## Abstract (arXiv)

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