Sensitivity Analysis of the SimQL Trustworthy Recommendation System

Flávia Pires; Moreira, António Paulo G. M.; Paulo Leitão; Leitão, Paulo; Pires, Flávia

http://hdl.handle.net/10198/34327

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Authors

Flávia Pires

Moreira, António Paulo G. M.

Paulo Leitão

Leitão, Paulo

Pires, Flávia

Abstract(s)

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.

Keywords

Manufacturing Decision-making Recommendation system Sensitivity analysis

URI

http://hdl.handle.net/10198/34327

Citation

Pires, F., Moreira, A.P., Leitão, P. (2024). Sensitivity Analysis of the SimQL Trustworthy Recommendation System. In 13th International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing (SOHOMA 2023). Cham: Springer Nature, p. 333-344. ISBN 978-303153444-7.