Deep Content-based Recommender Systems Exploiting Recurrent Neural Networks and Linked Open Data

UMAP (Adjunct Publication), pp. 239-244, 2018.

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Abstract:

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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