Docent: A Content-based Recommendation System To Discover Contemporary Art
Antoine Fosset, Mohamed El-Mennaoui, Amine Rebei, Paul Calligaro, Elise Farge di Maria, Hélène Nguyen-Ban, Francesca Rea, Marie-Charlotte Vallade, Elisabetta Vitullo, Christophe Zhang, Guillaume Charpiat, Mathieu Rosenbaum . Arxiv 2022 – 2 citations
[Paper]
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.