# Fashion Outfit Generation for E-commerce

## TL;DR

This paper presents a neural network model that learns style-compatible clothing item embeddings to generate fashion outfits, improving personalization and scalability in e-commerce recommendations.

## Contribution

The authors introduce a neural network-based approach trained on a large styled outfit dataset to generate compatible fashion outfits and perform the first AB test in this domain.

## Key findings

- Model achieves strong offline compatibility prediction performance.
- Generated outfits are preferred by users 21-34% more than baseline.
- First AB test demonstrates practical effectiveness of the approach.

## Abstract

Combining items of clothing into an outfit is a major task in fashion retail. Recommending sets of items that are compatible with a particular seed item is useful for providing users with guidance and inspiration, but is currently a manual process that requires expert stylists and is therefore not scalable or easy to personalise. We use a multilayer neural network fed by visual and textual features to learn embeddings of items in a latent style space such that compatible items of different types are embedded close to one another. We train our model using the ASOS outfits dataset, which consists of a large number of outfits created by professional stylists and which we release to the research community. Our model shows strong performance in an offline outfit compatibility prediction task. We use our model to generate outfits and for the first time in this field perform an AB test, comparing our generated outfits to those produced by a baseline model which matches appropriate product types but uses no information on style. Users approved of outfits generated by our model 21% and 34% more frequently than those generated by the baseline model for womenswear and menswear respectively.