# Exploring Content-based Artwork Recommendation with Metadata and Visual Features

arXiv: https://arxiv.org/abs/1706.05786 · HTML: https://mlsanity.com/paper/1706.05786/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

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

- [The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings](https://mlsanity.com/paper/2303.08182/) (2023)
- [Towards mapping the contemporary art world with ArtLM: an art-specific NLP model](https://mlsanity.com/paper/2212.07127/) (2022)

## Abstract (arXiv)

Compared to other areas, artwork recommendation has received little attention, despite the continuous growth of the artwork market. Previous research has relied on ratings and metadata to make artwork recommendations, as well as visual features extracted with deep neural networks (DNN). However, these features have no direct interpretation to explicit visual features (e.g. brightness, texture) which might hinder explainability and user-acceptance. In this work, we study the impact of artwork metadata as well as visual features (DNN-based and attractiveness-based) for physical artwork recommendation, using images and transaction data from the UGallery online artwork store. Our results indicate that: (i) visual features perform better than manually curated data, (ii) DNN-based visual features perform better than attractiveness-based ones, and (iii) a hybrid approach improves the performance further. Our research can inform the development of new artwork recommenders relying on diverse content data.