We have built a system to “classify clothes” automatically, with 175 million classified and correlated meta-products. This system allows us to automatically understand, manage and act upon any collection from any retailer (similarities, correlations, recommendations…)
To explain it easily, this system is like a brain that understands clothes and outfits, and allows you to organize and display products at your convenience, or your shopper’s convenience. It was constructed after analyzing millions of perfectly described outfits and fashion products uploaded to our system by different subsets of users of Chicisimo, and after analyzing how people interact with them.
The system converts fashion products into meta-products, which are abstractions of specific products of any catalog or closet. A fashion product is ephemeral, but its descriptors are not, so the system retains the value.
A meta-product is the most basic yet relevant description of a product, and one of the first tasks of our infrastructure is to convert any incoming fashion product into a meta-product. While a person might see a given garment, our system reads a set of descriptors, for example: burgundy + sweater + v-neck + comfy + casual + for school + size 42 + cashmere, etc.
For any given retailer, this system can automatically digest its catalogue and then, automatically: (i) Understand each product; (ii) Identify missing information; (iii) Identify similar products, defining similarity in a number of ways; (iv) Build complete looks mixing and matching the clothes in the collection; (v) Identify products that make sense to display together; (vi) Recreate any outfit with garments of the catalogue; (vii) display the correct products for each shopper, or for the current interest of each shopper; (viii) If the system detects a product that it cannot understand, it isolates the descriptor and incorporates it into the fashion ontology if the team so wishes.