Listar por autor "Landin, Alfonso"

Mostrando ítems 1-2 de 2

    • Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings 

      Landin, Alfonso; Parapar, Javier; Barreiro, Alvaro (Springer, 2020-04-08)
      [Abstract] Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of ...

    • Priors for Diversity and Novelty on Neural Recommender Systems 

      Landin, Alfonso; Valcarce, Daniel; Parapar, Javier; Barreiro, Álvaro (M D P I AG, 2019-07-31)
      [Abstract] PRIN is a neural based recommendation method that allows the incorporation of item prior information into the recommendation process. In this work we study how the system behaves in terms of novelty and diversity ...