Víctor Codina’s PhD thesis defended under 1000001 Labs’ supervision

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 […]