The huge interest in social networking applications – Friendster.com, for example, has more than 40 million users – led to a considerable research interest in using this data for generating recommendations. Especially recommendation techniques that analyze trust networks were found to provide very accurate and highly personalized results. The main contribution of this work is to extend the approach to trust-based recommendations, which up to now have been made for isolated items such as movies, to linked resources, in particular documents. Therefore, a second type of network, namely a document reference network, is considered apart from the trust network. This is, for example, the citation network of scientific publications. Recommendations for documents are typically made by reference-based visibility measures which consider a document to be the more important, the more often it is referenced by important documents. Document and trust networks, as well as ones like organization networks are integrated in a multi-layer network. This architecture allows for combining classical visibility measures with trust-based recommendations, giving trust-enhanced visibility measures. The trust-based recommender system for scientific publications SPRec implements a two-layer architecture and provides personalized recommendations via a web interface.