Personalized Social Image Recommendation Method Based on User-Image-Tag Model

Abstract

In the social image sharing websites (such as Flickr), users are allowed to upload images and tag them with tags. Due to the diversities of users' interests, different users may tag the same image with different tags. Therefore, tags not only reveal some important image semantic clues, but also show user's preference, which can provide a new effective solution for overcoming the semantic gap as well as realizing a personalized recommendation. In this paper, a personalized social image recommendation method based on user-image-tag model is proposed. The main contributions of our work are 1) to efficiently make use of tags, social image tags are re-ranked according to the image content; 2) to obtain user preference, a user-image-tag model is constructed with tripartite graph according to the correlation among users, images and top-ranking tags; and 3) a personalized social recommendation system is implemented based on user-image-tag model. Experimental results proved that our method can significantly improve the accuracy of personalized image recommendation.