Docent: A content-based recommendation system to discover contemporary art

Autor:	Fosset, Antoine, El-Mennaoui, Mohamed, Rebei, Amine, Calligaro, Paul, Di Maria, Elise Farge, Nguyen-Ban, Hélène, Rea, Francesca, Vallade, Marie-Charlotte, Vitullo, Elisabetta, Zhang, Christophe, Charpiat, Guillaume, Rosenbaum, Mathieu
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: submitted to NeurIPS2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.05648 Zobrazit plný text záznamu View this record from Arxiv