Characterizing user interest using heterogeneous media

It is often hard to accurately estimate interests of social media users because their messages do not have additional information, such as a category. In this paper, we propose an approach that estimates user interest from social media to provide personalized services. Our approach employs heterogeneous media to map social media onto categories. To describe the categories, we propose a hybrid method that integrates a topic model with TF-ICF for extracting both explicitly presented and implicitly latent features. Our evaluation result shows that it gives the highest performance, compared to other approaches. Thus, we expect that the proposed approach is helpful in advancing personalization of social media services.