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

arXiv: https://arxiv.org/abs/2207.05648 · HTML: https://mlsanity.com/paper/2207.05648/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Visually-Aware Fashion Recommendation and Design with Generative Image Models](https://mlsanity.com/paper/1711.02231/) (2017)
- [The Shape of Art History in the Eyes of the Machine](https://mlsanity.com/paper/1801.07729/) (2018)
- [News Recommender System: A review of recent progress, challenges, and opportunities](https://mlsanity.com/paper/2009.04964/) (2020)
- [Quantifying Creativity in Art Networks](https://mlsanity.com/paper/1506.00711/) (2015)
- [Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation](https://mlsanity.com/paper/2006.04275/) (2020)
- [Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models](https://mlsanity.com/paper/2201.01819/) (2022)
- [Natural Language Processing for Music Knowledge Discovery](https://mlsanity.com/paper/1807.02200/) (2018)
- [CuratorNet: Visually-aware Recommendation of Art Images](https://mlsanity.com/paper/2009.04426/) (2020)

## Built on by (most cited first, 2 of 2)

- [Towards mapping the contemporary art world with ArtLM: an art-specific NLP model](https://mlsanity.com/paper/2212.07127/) (2022)
- [Curating art exhibitions using machine learning](https://mlsanity.com/paper/2506.19813/) (2025)

## Abstract (arXiv)

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.