# The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings

arXiv: https://arxiv.org/abs/2303.08182 · HTML: https://mlsanity.com/paper/2303.08182/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [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)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation](https://mlsanity.com/paper/1607.04373/) (2016)
- [conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers](https://mlsanity.com/paper/2109.06501/) (2021)
- [CuratorNet: Visually-aware Recommendation of Art Images](https://mlsanity.com/paper/2009.04426/) (2020)
- [Exploring Content-based Artwork Recommendation with Metadata and Visual Features](https://mlsanity.com/paper/1706.05786/) (2017)
- [Personalised Visual Art Recommendation by Learning Latent Semantic Representations](https://mlsanity.com/paper/2008.02687/) (2020)

## Built on by (most cited first, 3 of 3)

- [HaHeAE: Learning Generalisable Joint Representations of Human Hand and Head Movements in Extended Reality](https://mlsanity.com/paper/2410.16430/) (2024)
- [Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane](https://mlsanity.com/paper/2408.10356/) (2024)
- [Curating art exhibitions using machine learning](https://mlsanity.com/paper/2506.19813/) (2025)

## Abstract (arXiv)

Artwork recommendation is challenging because it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and cognitive reflections they may trigger in users. In this paper, we focus on efficiently capturing the elements (i.e., latent semantic relationships) of visual art for personalized recommendation. We propose and study recommender systems based on textual and visual feature learning techniques, as well as their combinations. We then perform a small-scale and a large-scale user-centric evaluation of the quality of the recommendations. Our results indicate that textual features compare favourably with visual ones, whereas a fusion of both captures the most suitable hidden semantic relationships for artwork recommendation. Ultimately, this paper contributes to our understanding of how to deliver content that suitably matches the user's interests and how they are perceived.