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

利用图神经网络分析时尚产业的机器学习应用，评估Polyvore数据集中的衣着搭配和商品推荐准确性，并比较两种现有模型在任务完成和兼容性预测上的性能。我们发现Hypergraph Neural Network在两个任务上稍微具有更好的性能，并尝试使用视觉转换器生成的嵌入向量以提高预测准确性。

时尚推荐：使用GNN的服装搭配