Personalized Recommendation of Social Images by Constructing a User Interest Tree With Deep Features and Tag Trees

In view of the great diversity and complexity of social images, it is of great significance to improve the performance of personalized recommendation by learning a user interest from large-scale social images. Deep learning, as the latest research in the field of artificial intelligence, provides a new personalized recommendation solution of social images for learning a users interest. Moreover, social image sharing websites (such as Flickr) allow users to tag uploaded images with tags. As an important image semantic cue, effective tags not only represent the latent image information but also show personalized user interest. Therefore, a personalized recommendation method of social image is proposed by constructing a user-interest tree with deep features and tag trees in this paper. The main contributions of our paper are as follows: first, to efficiently make use of tags, a tag tree of social images is created by the re-ranked tags; second, for compactly representing the image content, deep features are learned by training the AlexNet network; third, a user-interest tree is constructed with deep features and tag trees that include the user-interest tree of social images and the user-interest tree of tags, respectively, and finally, a personalized recommendation system of social images is built based on a user-interest tree. Experiments on the NUS-WIDE dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized recommendations.