Cognitive fashion-ability score driven fashion merchandising acquisition

A system comprising:

• one or more computer processors;
• and
• a memory containing a program which when executed by the one or more computer processors performs an operation, the operation comprising:
• determining a first plurality of f-scores for a first plurality of products based on processing a first plurality of images using one or more convolutional neural networks (CNNs), wherein each of the first plurality of images corresponds to one of the first plurality of products, and wherein each of the first plurality of products is in an inventory;
• discretizing the first plurality of f-scores to generate a plurality of groups;
• generating, for each of the plurality of groups, a forecast based on historical data associated with each of the first plurality of products in each respective group;
• identifying one or more projected gaps in the inventory, based on the generated forecasts;
• determining a second plurality of f-scores for a second plurality of products based on processing a second plurality of images with the one or more CNNs, wherein each of the second plurality of products is not in the inventory;
• identifying, for each of the second plurality of products, a corresponding group in the plurality of groups based at least in part on the second plurality of f-scores;
• and
• selecting at least one product in the second plurality of products to order, based at least in part on determining that the identified corresponding group aligns with at least one of the one or more projected gaps in inventory.

A computer program product comprising a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising:

• determining a first plurality of f-scores for a first plurality of products in an inventory;
• discretizing the first plurality of f-scores to generate a plurality of groups;
• generating, for each of the plurality of groups, a forecast based on historical data associated with each of the first plurality of products in each respective group;
• determining a second plurality of f-scores for a second plurality of products, wherein each of the second plurality of products are not in the inventory;
• identifying, for each of the second plurality of products, a corresponding group in the plurality of groups based at least in part on the second plurality of f-scores;
• and
• selecting at least one product in the second plurality of products to order, based at least in part on the generated forecasts.