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Sensitivity Analysis of the SimQL Trustworthy Recommendation System

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Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1136))

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Abstract

The manufacturing domain faces a challenge in making timely decisions due to the large amounts of data generated by digital technologies such as Internet-of-Things, Artificial Intelligence (AI), Digital Twin, and Big Data. By integrating recommendation systems is possible to support the decision-makers in handling large amounts of data by delivering personalised, accurate, and quality recommendations. One example is the SimQL recommendation model that incorporates AI algorithms with trust and similarity measures to enhance recommendation quality. This paper aims to analyse the sensitivity of the SimQL model’s parameters, such as dataset conditions, trust and learning factors, and their impact on the final recommendation quality. A fuzzy logic approach is employed to evaluate the model and identify optimal operating conditions for the recommendation system. By implementing the findings of this study, manufacturers can improve the acceptance and adoption of the SimQL trustworthy recommendation system in this field.

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Acknowledgements

The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The author Flávia Pires thanks the Fundação para a Ciência e Tecnologia (FCT), Portugal, for the PhD Grant SFRH/BD/143243/2019.

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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    Flávia Pires & Paulo Leitão

  2. Laboratorio Associado para a Sustentabilidade e Tecnologia em Regioes de Montanha (SusTEC), Instituto Politecnico de Bragança, Campus Santa Apolonia, Bragança, 5300-253, Portugal

    Flávia Pires & Paulo Leitão

  3. INESC TEC - INESC Technology and Science, Faculty of Engineering, University of Porto, Porto, Portugal

    António Paulo Moreira

Authors
  1. Flávia Pires
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  2. António Paulo Moreira
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  3. Paulo Leitão
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Corresponding author

Correspondence to Flávia Pires .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control and Computer Science (Automatica), University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

  2. UPHF, LAMIH UMR CNRS 8201, Université Polytechnique Hauts de France, Valenciennes cedex 9, France

    Damien Trentesaux

  3. Polytechnic Institute of Bragança, Braganca, Portugal

    Paulo Leitão

  4. Polytech Annecy-Chambéry, LISTIC, Annecy, France

    Lamia Berrah

  5. SYMME, Polytech Annecy-Chambéry, Annecy, France

    Jose-Fernando Jimenez

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Pires, F., Moreira, A.P., Leitão, P. (2024). Sensitivity Analysis of the SimQL Trustworthy Recommendation System. In: Borangiu, T., Trentesaux, D., Leitão, P., Berrah, L., Jimenez, JF. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2023. Studies in Computational Intelligence, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-031-53445-4_28

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