Learning User’s Characteristics in Collaborative Filtering through Genetic Algorithms: Some New Results

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 312)

Abstract

This work presents an alternative approach (Genetic Algorithms approach) to traditional treatment of Recommender Systems (RSs). The work examines genetic algorithms possibilities to offer adaptive characteristics to these systems trough learning. The main goal, in addition to give a general view about RSs capabilities and possibilities, it is to provide a new example mechanism for to extend RSs learning capabilities (from user’s personal characteristics), with the purpose of improve the effectiveness at time of to find recommendations and appropriate suggestions for particular individuals.

Keywords

Recommender Systems Genetic Algorithms User-Adapted Interaction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Departamento de IngenieríasUniversidad de Bogotá, Jorge Tadeo LozanoBogotáColombia
  2. 2.Facultad de InformáticaUniversidad Politécnica de MadridMadridSpain

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