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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.

In my previous article, I have written about a content-based recommendation engine using TF-IDF for Goodreads data. In this article, I am using the same Goodreads data and build the recommendation engine using word2vec. Like the previous article, I am going to use the same book description to recommend books. The algorithm that we use always struggles to handle raw text data and it only understands the data in numeric form. In order to make it understand, we need to convert the raw text into numeric form.