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Advance Trends in Soft Computing pp 309–326Cite as

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

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Part of the book series: Studies in Fuzziness and Soft Computing ((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.

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Author information

Authors and Affiliations

  1. Departamento de Ingenierías, Universidad de Bogotá, Jorge Tadeo Lozano, Carrera 4 # 22-61, Bogotá, Colombia

    Oswaldo Velez-Langs

  2. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo, Madrid, Spain

    Angelica De Antonio

Authors
  1. Oswaldo Velez-Langs
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  2. Angelica De Antonio
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Corresponding author

Correspondence to Oswaldo Velez-Langs .

Editor information

Editors and Affiliations

  1. The University of Texas, San Antonio, Texas, USA

    Mo Jamshidi

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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© 2014 Springer International Publishing Switzerland

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Velez-Langs, O., De Antonio, A. (2014). Learning User’s Characteristics in Collaborative Filtering through Genetic Algorithms: Some New Results. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-03674-8_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03673-1

  • Online ISBN: 978-3-319-03674-8

  • eBook Packages: EngineeringEngineering (R0)

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