# Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation

## TL;DR

This study compares deep neural network features and attractiveness-based visual features for artwork recommendation, finding DNN features generally outperform EVF but some EVF features are more suited for specific tasks, with potential interpretability insights.

## Contribution

It provides a comparative analysis of DNN and EVF features in artwork recommendation and explores the interpretability of DNN features in relation to EVF.

## Key findings

- DNN features outperform EVF in recommendation accuracy
- Certain EVF features are more effective for physical artwork recommendation
- Some neurons in DNN encode visual features like brightness

## Abstract

Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation. Recent works have shown that visual features obtained from pre-trained deep neural networks (DNNs) perform very well for recommending digital art. Other recent works have shown that explicit visual features (EVF) based on attractiveness can perform well in preference prediction tasks, but no previous work has compared DNN features versus specific attractiveness-based visual features (e.g. brightness, texture) in terms of recommendation performance. In this work, we study and compare the performance of DNN and EVF features for the purpose of physical artwork recommendation using transactional data from UGallery, an online store of physical paintings. In addition, we perform an exploratory analysis to understand if DNN embedded features have some relation with certain EVF. Our results show that DNN features outperform EVF, that certain EVF features are more suited for physical artwork recommendation and, finally, we show evidence that certain neurons in the DNN might be partially encoding visual features such as brightness, providing an opportunity for explaining recommendations based on visual neural models.