Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios.
