Now showing items 1-5 of 5

    • Collaborative filtering process in a whole new light 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (IEEE, 2006)
      Collaborative Filtering (CF) Systems are gaining widespread acceptance in recommender systems and e-commerce applications. These systems combine information retrieval and data mining techniques to provide recommendations ...
    • Collaborative recommender systems: Combining effectiveness and efficiency 

      Symeonidis P; Nanopoulos A; Papadopoulos A; Manolopoulos Y (2008)
      Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the "information ...
    • Experimental evaluation of context-dependent collaborative filtering using item splitting 

      Baltrunas L; Ricci F (2014)
      Collaborative Filtering (CF) computes recommendations by leveraging a historical data set of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting their future ratings. This has been ...
    • Providing justifications in recommender systems 

      Symeonidis P; Nanopoulos A; Manolopoulos Y (2008)
      Recommender systems are gaining widespread acceptance in e-commerce applications to confront the "information overload"problem. Providing justification to a recommendation gives credibility to a recommender system. Some ...
    • Ratings in Recommender Systems: Decision Biases and Explainability 

      Coba, Ludovik (Free University of Bozen-Bolzano, 2020)
      Recommender systems are an application of artificial intelligence techniques where typically past behaviour of users is used to make predictions about their interests and to support them in identifying items they presumably ...