User interest-based recommender system for image-sharing social media

Nowadays, many people use social media to communicate with others, share their interests and obtain information. As the performance of the embedded cameras on mobile phones improve, image-sharing social media became a popular tool for people to communicate with others and share their interests, which yields vast amount of data related to the users' interests. However, only few studies pay attention to analyze data in image-sharing social media and utilize it to perform appropriate services, such as recommendation. We propose a framework to discover user interests using the Latent Dirichlet Allocation (LDA) based topic model and to recommend protentional friends and POIs related to the target user's interests. To do this, we devise the advanced LDA based topic model which can be utilized in image-sharing social media by exploiting both textual features and visual features. In addition, the novel method to discover user interest is proposed by generating topic graph to represent the user interest as graph-shape, which is an effective way to completely describe the user interest as explicit form. Lastly, we propose a method to recommend POIs and potential friends to the target user by calculating graph similarity between topic graphs. To demonstrate the superiority of our framework, we collected real data from image-sharing social media and conducted comparison experiments with state-of-the-art methods.