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Recommendations with Personality Traits Extracted from Text Reviews

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Intelligent Distributed Computing IX

Part of the book series: Studies in Computational Intelligence ((SCI,volume 616))

Abstract

It is well known that human reasoning and decision-making are strongly influenced by psychological aspects. Recent works explore the adoption of personality traits to provide personalized recommendations. In this article, we report experimental results obtained with implicit recognition of Big Five personality traits from users’ text reviews. Hence, we present a personality-based recommender system with the analysis of the overall users’ satisfaction regarding the list of recommended items, showing promising results.

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Acknowledgments

Special thanks to our participants for their cooperation.

Author information

Authors and Affiliations

  1. Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy

    Antonella Di Rienzo & Asana Neishabouri

Authors
  1. Antonella Di Rienzo
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  2. Asana Neishabouri
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Corresponding author

Correspondence to Antonella Di Rienzo .

Editor information

Editors and Affiliations

  1. Departamento de Informática/Centro ALGORITMI, Escola de Engenharia, Universidade do Minho, Braga, Portugal

    Paulo Novais

  2. Computer Science Department, Universidad AutĂłnoma De Madrid, Madrid, Spain

    David Camacho

  3. Departamento de Informática/Centro ALGORITMI, Escola de Engenharia, Universidade do Minho, Braga, Portugal

    Cesar Analide

  4. LIP6, University Pierre and Marie Curie 4, Paris Codex 05, France

    Amal El Fallah Seghrouchni

  5. Software Engineering Department, Faculty of Automatics, Computers and Electronics, University of Craiova, Craiova, Romania

    Costin Badica

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© 2016 Springer International Publishing Switzerland

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Di Rienzo, A., Neishabouri, A. (2016). Recommendations with Personality Traits Extracted from Text Reviews. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_33

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  • DOI: https://doi.org/10.1007/978-3-319-25017-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25015-1

  • Online ISBN: 978-3-319-25017-5

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