Docent: A content-based recommendation system to discover contemporary art
Abstract
Recommendation systems have been widely used in various domains such as music, films, e-shopping etc. After mostly avoiding digitization, the art world has recently reached a technological turning point due to the pandemic, making online sales grow significantly as well as providing quantitative online data about artists and artworks. In this work, we present a content-based recommendation system on contemporary art relying on images of artworks and contextual metadata of artists. We gathered and annotated artworks with advanced and art-specific information to create a completely unique database that was used to train our models. With this information, we built a proximity graph between artworks. Similarly, we used NLP techniques to characterize the practices of the artists and we extracted information from exhibitions and other event history to create a proximity graph between artists. The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists. After an assessment by a team of art specialists, we get an average final rating of 75% of meaningful artworks when compared to their professional evaluations.
Cite
@article{arxiv.2207.05648,
title = {Docent: A content-based recommendation system to discover contemporary art},
author = {Antoine Fosset and Mohamed El-Mennaoui and Amine Rebei and Paul Calligaro and Elise Farge Di Maria and Hélène Nguyen-Ban and Francesca Rea and Marie-Charlotte Vallade and Elisabetta Vitullo and Christophe Zhang and Guillaume Charpiat and Mathieu Rosenbaum},
journal= {arXiv preprint arXiv:2207.05648},
year = {2022}
}
Comments
submitted to NeurIPS2022