Modeling user personality traits from aesthetic preference on multiple images

In recent years, people have been spending more and more time on social media. Within the realm of multimedia contents used by platforms, the quantity of visuals is certainly growing in significance. Interaction data enables to know the users' favourite images. This information could be exploited to gain a deeper insight into their psychological profile, since the literature on automatic personality recognition suggests that personality traits may correlate with aesthetics. In this paper we explore the use of personal preference on multiple images to predict personality traits of users. Unlike previous works, we propose a model that exploits ResNet50, a Convolutional Neural Network, to automatically extract features from the images in the PsychoFlickr dataset. We then fit five independent linear regressors on these features to detect personality. In order to determine whether using more than one image leads to better results, we train the model multiple times, using one to five images as input, and we compare the performances. Our method seems to outperform the related state-of-The-Art works.