Can you recommend content to creatives instead of final consumers? A RecSys based on user's preferred visual styles

TL;DR

This paper presents a recommendation system tailored for content creators, focusing on visual style preferences rather than semantic content, addressing unique challenges in a global image marketplace.

Contribution

It introduces a novel RecSys that learns visual style preferences across projects, extending prior work with an evaluation setup for creative content recommendation.

Findings

Effective in capturing visual style preferences

Addresses rapid interest changes in creative users

Applicable in global image marketplaces

Abstract

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.