In this paper, we focus on advancing the multiplex network completion method using tensor structures through the Einstein product. We introduce a novel approach based on similarity tensors. Furthermore, we have developed a strategy to simultaneously factorize the adjacency tensor of the observed nodes. Additionally, we propose an optimization technique that employs alternating minimization to address the multiplex network completion challenge. We demonstrate the effectiveness of our method through applications on both synthetic and real-world datasets