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Intelligent Systems'2014 pp 213–224Cite as

AIRS: Ant-Inspired Recommendation System

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

The goal of recommendation systems is to produce a set of meaningful suggestions for a group of users that can be useful for them. This paper introduces a multi-agent algorithm that builds a distributed recommendation system by exploiting nature-inspired techniques. The recommendable resources are recognized through a metadata represented of a bit string obtained by the application of a locality preserving hash function that maps similar resources into similar strings. Each agent works independently to replicate and wisely relocate the metadata. The agent operations are led by the application of ad-hoc probability functions. The outcome of this collective work will be a sorted logical overlay network that allows a fast recommendation service. Experimental analysis shows how the logical reorganization of metadata achieved by the agents can improve the performances of the recommendation system.

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Author information

Authors and Affiliations

  1. CNR - ICAR, Institute for High Performance Computing and Networking, National Research Council of Italy, Via Pietro Bucci, 41C, 87036, Rende, CS, Italy

    Agostino Forestiero

Authors
  1. Agostino Forestiero
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Correspondence to Agostino Forestiero .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

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

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

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

    S. Zadrozny

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© 2015 Springer International Publishing Switzerland

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Forestiero, A. (2015). AIRS: Ant-Inspired Recommendation System. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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