  Nowadays, most recommender systems exploit user-provided ratings to infer
their preferences. However, the growing popularity of social and e-commerce
websites has encouraged users to also share comments and opinions through
textual reviews. In this paper, we introduce a new recommendation approach
which exploits the semantic annotation of user reviews to extract useful and
non-trivial information about the items to recommend. It also relies on the
knowledge freely available in the Web of Data, notably in DBpedia and Wikidata,
to discover other resources connected with the annotated entities. We evaluated
our approach in three domains, using both DBpedia and Wikidata. The results
showed that our solution provides a better ranking than another recommendation
method based on the Web of Data, while it improves in novelty with respect to
traditional techniques based on ratings. Additionally, our method achieved a
better performance with Wikidata than DBpedia.
