A Taxonomy of Collaborative-based Recommender Systems

TítuloA Taxonomy of Collaborative-based Recommender Systems
AutoresF. Pérez, E. Sánchez
TipoCapítulo de libro
Fonte Web Personalization in intelligent Environments, Berlin (Germany), Springer-Verlag, Vol. 229, pp. 81-117 , 2009.
ISBN978-3-642-02793-2
AbstractThe explosive growth in the amount of information available in the WWW and the emergence of e-commerce in recent years has demanded new ways to deliver personalized content. Recommender systems [51] have emerged in this context as a solution based on collective intelligence to either predict whether a particular user will like a particular item or identify the collection of items that will be of interest to a certain user. Recommender systems have an excellent ability to characterize and recommend items within huge collections of data, what makes them a computerized alternative to human recommendations. Since useful personalized recommendations can add value to the user experience, some of the largest e-commerce web sites include recommender engines. Three well known examples are Amazon.com [1], LastFM [4] and Netflix [6].