  Fashion preference is a fuzzy concept that depends on customer taste,
prevailing norms in fashion product/style, henceforth used interchangeably, and
a customer's perception of utility or fashionability, yet fashion e-retail
relies on algorithmically generated search and recommendation systems that
process structured data and images to best match customer preference. Retailers
study tastes solely as a function of what sold vs what did not, and take it to
represent customer preference. Such explicit modeling, however, belies the
underlying user preference, which is a complicated interplay of preference and
commercials such as brand, price point, promotions, other sale events, and
competitor push/marketing. It is hard to infer a notion of utility or even
customer preference by looking at sales data.
  In search and recommendation systems for fashion e-retail, customer
preference is implicitly derived by user-user similarity or item-item
similarity. In this work, we aim to derive a metric that separates the buying
preferences of users from the commercials of the merchandise (price,
promotions, etc). We extend our earlier work on explicit signals to gauge
sellability or preference with implicit signals from user behaviour.
