Ranking in heterogeneous social media

The problem of image search has been studied extensively in recent years because of the large and increasing repositories of images on the web, social media, and other linked networks. Most of the available techniques for keyword-based image search on the web use the text in the surrounding or linked text in order to retrieve related images. Many image repositories on the web are built upon social media platforms such as Flickr. Such platforms provide a rich level of information in terms of the user linkage information to images, tags or other comments which are contributed by the users. It is reasonable to assume that the content of the images, users and other social cues such as tags and comments are often related to one another. Therefore, such cues can be useful for improving the effectiveness of search and ranking algorithms. In this paper, we propose SocialRank, which is a technique for using social hints in order to improve the image search and ranking process. Furthermore, we propose a holistic framework to combine social tags, social network text, linkage between actors and images, as well as the actual image features in order to create a ranking technique for image search. We design a PageRank-like method which can combine these different methods in order to provide an effective method for image search and ranking in social networks.