Abstract: The visual perception recommendation system aims to enhance the behavioral features of user-item interaction by extracting the visual features of item images from the perspective of visual cognition,and model the user’s visual and behavior-rela-ted preferences,so as to make better recommendations.In the existing visual perception recommendation research,pre-trained convolutional neural network(CNN) is usually used to extract the semantic features of visual objects,and the hidden aesthetic style features inside the appearance image of the item are rarely considered.In addition,the embedded information of user-item interaction behavior structure is usually ignored in visual perception recommendation.To address these issues,an aesthetic feature-aware visual recommendation system is proposed that fuses image aesthetics and behavioral interaction structure embeddings(ABVR).ABVR uses the pre-trained ViT model to extract the high-level visual features of the image－semantic category features,uses the aesthetic extraction network to mine the middle-level aesthetic visual features in the image－－the color,shapes and other features of the items,and uses the graph convolution neural network(GCN) module to learn the multi-layer graph structure embedding features of user item interaction graph nodes,and finally associates and fuses the three types of features to achieve aesthetically enhanced visual recommendations.Extensive experiments are conducted on two real datasets to verify the effectiveness of the ABVR model in improving visual recommendation performance.

Key words: Visual perception, Aesthetic features, Visual recommendation, Graph convolutional neural networks