In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD (Linked Open Data) compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.