Abstract
The digital twin has been gaining significant attention from the academia and industry sectors in the last few years. The digital twin concept enables monitoring, diagnosis, optimisation, and decision support tasks to improve industrial systems operation. One of the identified challenges in this field is the need to improve the decision support cycle by decreasing decision-making time and improving the accuracy of recommendations by considering human intervention in the cycle. Bearing this in mind, the paper explores the use of trust models to improve the recommendation cycle in the digital twin. For this purpose, a literature overview on trust applied in recommendation systems was performed, focusing on the concept, its properties and previous models. Considering this analysis, a trust-based model is specified in a digital twin artificial intelligence-based recommendation system.
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Acknowledgements
This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020. The author Flávia Pires thanks the Fundação para a Ciência e Tecnologia (FCT), Portugal for the Ph.D. Grant SFRH/BD/143243/2019.
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Pires, F., Moreira, A.P., Leitao, P. (2022). Trust Model for Digital Twin Based Recommendation System. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_11
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