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David – A Novel Approach to an Industrial Recommender System

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Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection (PAAMS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1678))

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Abstract

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. These types of users, which differ from standard ones, are fuzzy and vague in their choices, avoiding explicit forms of feedback. This paper addresses state-of-the-art of such industrial systems, its users and explores a potential solution.

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Acknowledgements

This research work was supported by National Funds through FCT (Fundação para a Ciência e a Tecnologia) under the project UI/DB/00760/2020.

Author information

Authors and Affiliations

  1. GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP – Instituto Superior de Engenharia do Porto, Porto, Portugal

    Rui Marques & Constantino Martins

  2. USAL - Universidad de Salamanca, Salamanca, Spain

    María N. Moreno-García

Authors
  1. Rui Marques
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  2. Constantino Martins
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  3. María N. Moreno-García
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Corresponding author

Correspondence to Rui Marques .

Editor information

Editors and Affiliations

  1. University of Salamanca, Salamanca, Spain

    Alfonso González-Briones

  2. Institute of Engineering - Polytechnic of Porto, Porto, Portugal

    Ana Almeida

  3. Universidad Rey Juan Carlos, Madrid, Spain

    Alberto Fernandez

  4. German International University, Cairo, Egypt

    Alia El Bolock

  5. University of Minho, Braga, Portugal

    Dalila Durães

  6. Universitat Politècnica de València, Valencia, Spain

    Jaume Jordán

  7. National Laboratory of Energy and Geology, Amadora, Portugal

    Fernando Lopes

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Marques, R., Martins, C., Moreno-García, M.N. (2022). David – A Novel Approach to an Industrial Recommender System. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Communications in Computer and Information Science, vol 1678. Springer, Cham. https://doi.org/10.1007/978-3-031-18697-4_2

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  • DOI: https://doi.org/10.1007/978-3-031-18697-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18696-7

  • Online ISBN: 978-3-031-18697-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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