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Profiting from Several Recommendation Algorithms Using a Scalable Approach

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NEO 2015

Part of the book series: Studies in Computational Intelligence ((SCI,volume 663))

Abstract

This chapter proposes the use of a scalable platform to run a complex recommendation system. We focus on a system made up of several recommendation algorithms which are run as an offline process. This offline process generates user profiles that represent which algorithm should provide the recommendations to a given user and item, and will be combined with a fuzzy decision system to generate every recommendation. Yet, given the amount of data that will be processed and the need to run that offline process frequently, we propose to reduce execution time by using Hadoop, a scalable, distributed and fault-tolerant platform. Obtained results shows how the main goal pursued here is achieved: the efficient use of computer resources which allows for a significant reduction in computing time.

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Acknowledgments

This work has been supported by FP7-PEOPLE-2013-IRSES, Grant 612689 ACoBSEC, Spanish Ministry of Economy, Project UEX:EPHEMEC (TIN2014-56494-C4-2-P) and CDTI project Smart Cities & Mobile Technologies; Junta de Extremadura, and FEDER, project GR15068. It has also been supported by CONACyT México by the project 155045 – “Evolución de Cerebros Artificiales en Visión por Computadora” and TESE by the project DIMI-MCIM-004/08.

Author information

Authors and Affiliations

  1. European Organisation for Nuclear Research (CERN), Geneva, Switzerland

    Daniel Lanza

  2. University de Extremadura, Badajoz, Spain

    F. Chávez & Francisco Fernandez

  3. Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    M. Garcia-Valdez & Leonardo Trujillo

  4. Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, Ensenada, Mexico

    Gustavo Olague

Authors
  1. Daniel Lanza
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  2. F. Chávez
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  3. Francisco Fernandez
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  4. M. Garcia-Valdez
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  5. Leonardo Trujillo
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  6. Gustavo Olague
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Corresponding author

Correspondence to Daniel Lanza .

Editor information

Editors and Affiliations

  1. Departamento de Comput Cinvestav-IPN, Mexico, Mexico

    Oliver Schütze

  2. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  4. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Mexico

    Yazmin Maldonado

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Lanza, D., Chávez, F., Fernandez, F., Garcia-Valdez, M., Trujillo, L., Olague, G. (2017). Profiting from Several Recommendation Algorithms Using a Scalable Approach. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds) NEO 2015. Studies in Computational Intelligence, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-319-44003-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-44003-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44002-6

  • Online ISBN: 978-3-319-44003-3

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