all AI news
Can you recommend content to creatives instead of final consumers? A RecSys based on user's preferred visual styles. (arXiv:2208.10902v1 [cs.CV])
Aug. 24, 2022, 1:11 a.m. | Raul Gomez Bruballa, Lauren Burnham-King, Alessandra Sala
cs.LG updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Lead Data Modeler
@ Sherwin-Williams | Cleveland, OH, United States