Deep content-based recommender systems exploiting recurrent neural networks and linked open data

In this paper we present a deep content-based recommender system (DeepCBRS) that exploits Bidirectional Recurrent Neural Networks (BRNNs) to learn an effective representation of the items to be recommended based on their textual description. Next, such a representation is extended by introducing structured features extracted from the Linked Open Data (LOD) cloud, as the genre of a book, the director of a movie, in order to enrich the available content with new and very descriptive information. In the experimental session we evaluate the effectiveness of our approach in a top-N recommendation scenario: First, we compare the representation based on BRNNs to that obtained through different deep learning techniques. Next, we demonstrate that the exploitation of features gathered from the LOD cloud improves the overall accuracy of our DeepCBRS.