About a year ago, we shared our approach and our vision on how we’ve applied machine learning to helping women plan their outfits and use a virtual closet. We didn’t have a blog back then, so we shared our thoughts on Medium. Now, we want to link to it from here.
In that post, we focused on the following aspects:
- Our vision, and how we think that outfits are the best asset to understand people’s what to wear needs. Understanding clothing behaviour will transform online fashion, and will enable the creation of outfit planners, virtual closets and smart wardrobes, closet organizers, truly personalized smart mirrors and fitting rooms, and other fashion retail omnichannel personalization experiences;
- Our approach to learning how to help people with the right outfit ideas. Learning both from people and from data;
- Our approach to learning from outfit data, how we built a fashion and clothes ontology, and a data graph.
What is Omnichannel Personalization?
Please visit the Medium post How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach or read the Hacker News discussion. You can also read about our taste graph here.
Further reading: