Visual Styles RecSys: Learning Users’ Preferred Visual Styles in an Image Marketplace
After my PhD I joined Shutterstock where I’ve been working on Recommender Systems. In this work I present Visual Styles RecSys, a model that learns users’ visual style preferences transversal to the projects they work on, and which aims to personalise the content served at Shutterstock. It was presented as an oral in ACM RecSys ‘22 industrial track.
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.
Although I advice reading the ACM RecSys 2022 paper Learning Users’ Preferred Visual Styles in an Image Marketplace or the extended technical report Can you recommend content to creatives instead of final consumers? A RecSys based on user’s preferred visual styles available in arXiv, different materials have been created to disseminate this work, which I share next.
