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International Conference on Exploring Services Science

IESS 2020: Exploring Service Science pp 65–78Cite as

Collaborative Recommendations with Deep Feed-Forward Networks: An Approach to Service Personalization

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 377))

Abstract

The aim of this article is to discuss an advanced approach to recommendation systems, based on the adoption of Deep Feed-Forward Neural Networks. Recommendation engines are data-driven infrastructures designed to help customers in their decision-making process, and nowadays represent the “state of the art” in designing smart and personalized services, in accordance with the new customer-centric perspective. For this purpose, we followed a quantitative methodological approach, comparing the predictive ability of traditional “Collaborative” recommendation algorithms, like the k-Nearest Neighbors (k-NN) and the Singular Value Decomposition (SVD), with Feed-Forward Neural Networks; given these assumptions, we finally demonstrated that a “Deep” Neural architecture could achieve better results in terms of “loss” generated by the model, laying the foundations for a new, innovative paradigm in service recommendation science.

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Author information

Authors and Affiliations

  1. LUISS University of Rome, Rome, Italy

    Giovanni Luca Cascio Rizzo & Pasquale De Rosa

  2. International Telematic University UNINETTUNO, Rome, Italy

    Marco De Marco & Luigi Laura

Authors
  1. Giovanni Luca Cascio Rizzo
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  2. Marco De Marco
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  3. Pasquale De Rosa
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  4. Luigi Laura
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Corresponding author

Correspondence to Luigi Laura .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Henriqueta Nóvoa

  2. University Politehnica of Bucharest, Bucharest, Romania

    Monica Drăgoicea

  3. Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Niklas Kühl

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Rizzo, G.L.C., De Marco, M., De Rosa, P., Laura, L. (2020). Collaborative Recommendations with Deep Feed-Forward Networks: An Approach to Service Personalization. In: Nóvoa, H., Drăgoicea, M., Kühl, N. (eds) Exploring Service Science. IESS 2020. Lecture Notes in Business Information Processing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-38724-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-38724-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38723-5

  • Online ISBN: 978-3-030-38724-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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