The problem
When it comes to apparel, there are adventurous and conservative shoppers. Adventurous shoppers spend hours researching, browsing, experimenting, and trying on different styles of fashion, and have fun doing it. Conservative shoppers just want to occasionally purchase slightly different hues of the clothes they have worn for years, and the idea of shopping fills them with dread. They like to stick with the familiar because they often cannot visualize how different styles of clothes can look. The problem is often not that these shoppers are unwilling to wear different apparel, but that they are unwilling to put in the search costs. The unwillingness to experiment constrains the growth the $225bn apparel market and contributes to a fashionably duller world.
Our wardrobes for example
The solution
The team proposes an augmented judgment system that uses a shopper’s non-apparel preferences to predict apparel preferences that they may not be aware of. For example, if [Wear] knows that a shopper likes James Bond movies, enjoys wine tastings, drives a Mercedes, spends 45 minutes every morning for personal grooming, and prefers to eat at Grace, it would predict the type of apparel the shopper would like through a model that connects non-apparel preferences toapparel tastes. [Wear] would then remove apparel styles already owned by the shopper from the output to form a set of recommendations of new styles the shopper may like. The result would be distinct from that produced for a shopper who likes Mission Impossible movies, enjoys dive bars, rides a Harley Davidson, spends 5 minutes on personal grooming, and prefers to eat at Smoque BBQ. This model will use augmented perception techniques to understand different parts of the user’s environment that provide insight into their preferences. This algorithm will generate apparel recommendations that fit the needs and preferences of shoppers who may not understand their own preferences, leading to higher consumer willingness to purchase new apparel and higher industry sales.
The demonstration
The team would like to produce a working prototype of the system to illustrate its effectiveness in real time. The team envisions asking an audience member to fill out a basic survey on non-apparel preferences. The audience member would then be asked to predict a shirt he would like the most from a selection of 10 shirts, while the [Wear] algorithm will simultaneously predict his preferences. The person would then be presented with both shirts to try on and provide feedback on which one he liked more.
Sources:
https://www.statista.com/topics/965/apparel-market-in-the-us/
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