Abstract
ABSTRACT Digital twin models are computerized clones of physical assets or systems and have attracted much attention from academia and industries. Digital twin applications focus on smart manufacturing systems. Meanwhile, manufacturing products are driven increasingly by the needs of customers. Industrial production modes have evolved from mass production to personalized production. Understanding customers and meeting their personalized needs have become important issues in smart manufacturing. Social networks provide platforms for online customers to engage in different behaviors. In addition, personality recognition is a crucial issue for understanding people. In this study, a new technique is proposed to formalize personality as digital twin models by observing users’ posting content and liking behavior. A multitask learning deep neural network model is used to predict users’ personality through two types of data representation. Experimental results show that combining the two types of data can improve personality prediction accuracy.
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