You could initially present a user some images to assess it's preferences and use some recommender system (collaborative filtering), a la Netflix.

That could give you an starting point. But I believe the main issue is that fashion products have, by definition, short shelf life, so you can't run the algos on SKU data. Then you can use deep learning on product images + user categorical data to try to predict preferences, maybe simple binary classification?

I guess using images as input should give better features than textual description.

> You could initially present a user some images to assess it's preferences and use some recommender system (collaborative filtering), a la Netflix.

You would have to tag the hell out of these photos, right? Disambiguating preferences is the challenge -- the user may have liked images #3 and #7, but why? Specific items, or the color palette, or the silhouette, or just the model? A post by Chicisimo on the front page addresses these hurdles [1].

I've also seen some of these image recognition apps in action -- they pick up on patterns and color very well, but struggled with silhouette.

[1] https://hackernoon.com/how-we-grew-from-0-to-4-million-women...

Seeing what people themselves like doesn't sound a good way to determine what fashion they'd buy. Fashion is about a consensus largely established by a cabal of sellers; users surely want to find out what is fashionable? Fashion is mainly about what other people like.

Oh, that's largely a myth. These seller do have some influence, but it's much less important than what they want you to perceived. Fashion tastes comes from a lot of sources and some is part of your identity.

That's part of what makes this industry hard.

Ask them for access to their Facebook photos, get them to tag themselves and then build an embedding of their fashion choices.