Now showing items 1-5 of 5

    • A hybrid recommender system for industrial symbiotic networks 

      Gkatzioura, Anna; Sànchez-Marrè, Miquel; Gibert, Karina (2018)
      Conference lecture
      Open Access
      Various solutions enabling the realization of synergies in Industrial Symbiotic Networks have been proposed. However, incorporating intelligence into the platforms that these networks use, supporting the involved actors ...
    • A study on contextual influences on automatic playlist continuation 

      Gkatzioura, Anna; Sànchez-Marrè, Miquel; Jorge, Alípio Mário (2018)
      Conference lecture
      Open Access
      Recommender systems still mainly base their reasoning on pairwise interactions or information on individual entities, like item attributes or ratings, without properly evaluating the multiple dimensions of the recommendation ...
    • Design and Implementation of a Customer Personalised Recomender System 

      Gkatzioura, Anna (Universitat Politècnica de Catalunya, 2013-06)
      Master thesis (pre-Bologna period)
      Open Access
      [ANGLÈS] Market basket analysis is examined through the application of probabilistic topic models and case-based reasoning in order to provide more insight into customer buying habits and generate meaningful recommendations.
    • A hybrid approach for item collection recommendations : an application to automatic playlist continuation 

      Gkatzioura, Anna (Universitat Politècnica de Catalunya, 2018-11-23)
      Doctoral thesis
      Open Access
      Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are ...
    • Using contextual information in music playlist recommendations 

      Gkatzioura, Anna; Sànchez-Marrè, Miquel (IOSPress, 2017)
      Part of book or chapter of book
      Open Access
      Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in,at a given moment. However, the majority of Recommender ...