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Our unsupervised learning model

Capturing, refining and providing valuable output through fashion digital interfaces Background: Chicisimo built the interfaces and infrastructure to help people find what outfit to wear, and what garment to buy. We focused on (i) creating input interfaces that allow people to express a need; on (ii) building the data system that understands and responds to […]

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Taste Profiles of each shopper

WHAT IS A TASTE PROFILE? A Taste Profile summarizes the taste of an individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases. OUR SERVICES We help you build a Taste Profile for each of your shoppers, as part of your Taste Graph. Taste Profiles include: 1. We help you […]

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Omnichannel Personalization with a Fashion Taste Graph

Omnichannel Personalization with a Fashion Taste Graph allows retailers to understand and classify each product in their catalogue, and each of their shoppers. How did we build our Fashion Taste Graph for Omnichannel Personalization Through Chicisimo’s consumer apps, we’ve received millions of described outfits, the described clothes in millions of closets, and hundreds of millions […]

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Fashion ontology

Our fashion ontology is our tool to understand products and people, and we built it to respond to very specific needs in our mobile apps. Why we built our fashion ontology? Fashion lacks a standard to classify clothes or to refer to the variety of concepts that describe products, styles, and personal fashion preferences. At […]

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Smart Virtual Closet Technology

We have built and shipped to 5M users a smart virtual closet that allows women to easily digitize most of their clothes in minutes. We have learnt that with this approach, the shopper is absolutely retained. Our Smart Virtual Closet Technology allows your shoppers to digitally store all their physical clothes, without any friction, in their […]

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Real fashion on real people ®

Chicisimo has built a learning system that classifies clothes and understands people’s taste, by automatically learning from users’ outfit data. With 5M users, we’ve built a method to learn fast and iterate, and just on iOS we’ve shipped 204 public releases and 919 pre-production releases. Read about our tech, taste graph and ontology here, and […]