Deep Content-based Recommender Systems Exploiting Recurrent Neural Networks and Linked Open Data
UMAP (Adjunct Publication), pp. 239-244, 2018.
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 Link...More
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