At the Fashion Taste API, we are building omnichannel personalized experiences on top of the Taste profile of each shopper. This infrastructure has several components, such as a fashion ontology, a taste graph for each fashion retailer or digital closet technology, which fashion retailers can offer to their customers.
With this digital closet technology, women can easily recreate their physical closet, storing their clothes digitally. The closet then shows people how they can combine their clothes based on our analysis of millions of described outfits uploaded to our consumer apps, by real women. The closet also shows them how other people wear the clothes they have, or similar ones.
Bringing your clothes to your shopping experience
Today, we are introducing an in-store outfit recommender system. Built on top of the Fashion Taste API, it is based on a system to match a user’s clothes with any external garment regardless of the source.
In the video below, you can see my colleague Maria looking at clothes at a physical store, and how the Fashion Taste API recommends what outfits she can create with the clothes in her closet and the garment she is about to buy.
This is the process: We are reading the QR, extracting the images, and sending an image to our system where a deep learning algorithm extracts descriptors of the garment. Think of these descriptors as a “bag of descriptors”. This bag of descriptors is then sent to our taste graph which tells us how it correlates with the clothes in the user’s closet.
An ontology and a taste graph to enable automation of outfit advice
The asset that allows us to interpret any incoming garment is our ontology. It also works with outfits and text.
The Fashion Taste API ontology is the classification of descriptors needed to define an outfit, in the terms that are relevant for women, speaking the vocabulary they use. We’ve created this ontology through the analysis of how women search for outfits, how they tag them, and how they classify them.
(Among other things) Our ontology allows us to convert any garment into “metagarments”. Metagarments are the most basic yet relevant description of garments (like bags of descriptors), and they allow our system and algorithms to have a clear understanding of 100% of incoming garments. It is this understanding what allows the taste graph to be effective at doing its job, and match the garment with other garments. As we know what garments people have in their closet, we filter by those, and return a complete outfit. With this, building a personalization platform for fashion retail is a reality.
We see this infrastructure applied to fitting rooms, smart mirrors in our bedrooms, and while shopping online. The learning here is that, while clothes are chaotic from the point of view of classification and capturing, there is a way to automatically bring clean, structured data to clothes and fashion taste. This changes everything, because it’s a new beginning.
Outfits provide a unique perspective into taste
We’ve been working on recommender systems since 2004 in different verticals. In fashion, the challenge we found interesting is how to capture the data and then discern the underlying taste. Our thesis is that online fashion will be transformed by a tool that understands taste. Because if you understand taste, you can delight people.
“Outfits” are the asset that allows taste to be understood. They bring the context that lead people to describe their clothes, their what-to-wear needs, and other relevant taste descriptors.
While the traditional approach to recommender systems for fashion focus on suggesting you more products to buy without understanding your taste, we believe that the focus should be different. And that’s our focus: understanding people at an individual level: their needs, behaviour and taste.
The future of fashion looks outstanding: New hardware, new sources of input, a better understanding of people…
The future of fashion looks outstanding, we believe. Each of us will have our own automated personal stylist, accessible both via a mobile app and via a piece of hardware in our bedroom. This system will store our clothes and our behaviour, originated from sources of input with little friction.
Based on this understanding, this personal stylist will help us feel well and confident with our outfits and ourselves. That’s the objective.
Learn more about the Fashion Taste API.
Thanks for reading! 😍