A recommendation approach for consuming linked open data

Most of linked open data (LOD) applications focus on the search and visualization of information, not efficiently using the links among objects in different data sources and the semantics of their relations. This work aims to create a LOD-consuming approach that uses recommendation techniques based on items' description, their relations, users' interests and social network. The proposed approach was instantiated by an application that uses movie related LOD. The results obtained in our experiments were promising: accuracy of the recommendations generated was equal or better, compared to other recommender algorithms used in conventional (not LOD) scenario.