Image recommender system based on compact convolutional transformer image style recognition

Abstract

Social network users face a large number of images; therefore, it is very important that social media finds and recommends images that satisfy the user’s preferences properly. This article investigates the effect of using image style on the performance of a social image recommender system. The effect of distinctive visual styles is evident in art, photography, film, and advertising; however, to the best of our knowledge, it has not been used in social image recommendation systems. We propose image style recognition as a component of user preferences. To do this, we first propose a method based on deep features and compact convolutional transformer to recognize the style of images; then, an image recommender system based on an image style recognition is presented. We consider 15 distinct styles from the Flickr Style dataset, consisting of five categories: atmosphere, mood, genre, composition styles, and color. The experimental results show that style has a positive effect and can significantly improve Recall@k and Precision@k by ∼5 % − 10 % (for the 100 top images recommended) for personalized image recommendation compared with not using the.