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REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce

  • Conference paper
Intelligent Distributed Computing VII

Part of the book series: Studies in Computational Intelligence ((SCI,volume 511))

Abstract

Recommender systems usually support B2C e-Commerce activities without to provide e-buyers with information about the reputation of both products and interlocutors. To provide B2C traders with suggestions taking into account gossips, in this paper we present REBECCA, a fully decentralized trust-based B2C recommender system that also guarantees scalability and privacy. Some experiments show the advantages introduced by REBECCA in generating more effective suggestions.

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Author information

Authors and Affiliations

  1. University Mediterranea of Reggio Calabria, 89123, Reggio Calabria, Italy

    Domenico Rosaci & Giuseppe M. L. Sarné

Authors
  1. Domenico Rosaci
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  2. Giuseppe M. L. Sarné
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Corresponding author

Correspondence to Domenico Rosaci .

Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University in Prague, Prague, Czech Republic

    Filip Zavoral

  2. Department of Computer Engineering, Yeungnam University, Gyeingsan, Korea, Republic of (South Korea)

    Jason J. Jung

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

    Costin Badica

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© 2014 Springer International Publishing Switzerland

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Rosaci, D., Sarné, G.M.L. (2014). REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce. In: Zavoral, F., Jung, J., Badica, C. (eds) Intelligent Distributed Computing VII. Studies in Computational Intelligence, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-319-01571-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-01571-2_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01570-5

  • Online ISBN: 978-3-319-01571-2

  • eBook Packages: EngineeringEngineering (R0)

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