Víctor Codina addresses current issues related to how the distributional semantics of concepts describing the entities of the recommendation space can be exploited to mitigate the data-sparsity problem and improve the prediction accuracy with respect to state-of-the-art recommendation techniques. Víctor also throws light on two novel semantically-enhanced prediction models that address the sparsity-related limitations: (1) a content-based […]