  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.
