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Source: multithreaded.stitchfix.com 

A core methodology at Stitch Fix is blending recommendations from machines with judgments of expert humans. Our machines produce recommendations via algorithms operating over structured data, while our human stylists curate and modify these recommendations on the basis of unstructured data and knowledge that isn’t yet reflected in our dataset (e.g., new fashion trends). This helps us choose the best 5 items to offer each client in each fix. The success of this strategy within our styling organization prompts consideration of how machines and humans might be brought together in the realm of fashion design. In this post we describe one implementation of such a system. In particular, we explore how the system could be implemented with respect to a target client segment and season. Fashion design is normally achieved by a qualitative process focused on stylistic intent and inspiration. In contrast, our team conceptualizes the design process as an algorithm searching for desirable regions (for example, points that yield maximal positive responses from our clients) within the space of possible blouses. Put another way, designing a new blouse can be thought of as searching a space with dimensions that correspond to attributes of a blouse like color, print, material fabrication, chest diameter or type of neckline.

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