Characterising Concepts of Interest Leveraging Linked Data and the Social Web

Extracting and representing user interests on the Social Web is becoming an essential part of the Web for personalisation and recommendations. Such personalisation is required in order to provide an adaptive Web to users, where content fits their preferences, background and current interests, making the Web more social and relevant. Current techniques analyse user activities on social media systems and collect structured or unstructured sets of entities representing users' interests. These sets of entities, or user profiles of interest, are often missing the semantics of the entities in terms of: (i) popularity and temporal dynamics of the interests on the Social Web and (ii) abstractness of the entities in the real world. State of the art techniques to compute these values are using specific knowledge bases or taxonomies and need to analyse the dynamics of the entities over a period of time. Hence, we propose a real-time, computationally inexpensive, domain independent model for concepts of interest composed of: popularity, temporal dynamics and specificity. We describe and evaluate a novel algorithm for computing specificity leveraging the semantics of Linked Data and evaluate the impact of our model on user profiles of interests.