Using Social Network Information to Identify User Contexts for Query Personalization

In recent years, social networks have gained a huge popularity among internet users, serving diverse purposes and communities. Meanwhile, in data-oriented applications, the increasing amount of available data has made it hard for users to find the information they need in the way they consider relevant. To help matters, a user-centric approach may be used to enhance query answering and, particularly, provide query personalization. In this work, we address the issue of personalizing query answers in data-oriented applications considering the user context provided by social network information. To this end, we propose a context-aware plugin named CODI4In. The CODI4In extracts users' social network information regarding their "likes" and use them as context information to provide query personalization. In this paper, we present the developed approach and some experimental results we have accomplished with real users. These results show that by considering the acquired user context really enhances the degree of relevancy of the obtained personalized answers.