  Providing meaningful recommendations in a content marketplace is challenging
due to the fact that users are not the final content consumers. Instead, most
users are creatives whose interests, linked to the projects they work on,
change rapidly and abruptly. To address the challenging task of recommending
images to content creators, we design a RecSys that learns visual styles
preferences transversal to the semantics of the projects users work on. We
analyze the challenges of the task compared to content-based recommendations
driven by semantics, propose an evaluation setup, and explain its applications
in a global image marketplace.
  This technical report is an extension of the paper "Learning Users' Preferred
Visual Styles in an Image Marketplace", presented at ACM RecSys '22.
