Content annotation and enrichment within LinkedTV produces arbitrarily large amounts of quality links to the web, which on the one hand shows the potential of the involved algorithms, but on the other hand can be overwhelming for a single user if not filtered and priorized beforehand. In this deliverable, we present our approaches to rank and filter these links based on a user’s interest. We offer solutions for implicitly learned interests, and for explicitely given preferences, by exploiting the user-centered ontologies as defined in Deliverable D4.4. Further, we explore ranking mechanisms directly based on the entities derived in the annotation and enrichment process. Finally, we offer quantitative and qualitative experiments and assessments on data drawn from the news broadcast scenario.