The insights from this work can be applied to other domains beyond temporal knowledge graphs where tensor decomposition is used for representation learning. For example, in recommender systems, tensor decomposition methods are commonly used to model user-item interactions over time. By applying the proposed method to mitigate heterogeneity among factor tensors in recommender systems, the model can better capture the dynamics of user preferences and item characteristics, leading to improved recommendation accuracy. Similarly, in natural language processing tasks such as text classification or sentiment analysis, tensor decomposition techniques can benefit from addressing heterogeneity among word embeddings or document representations to enhance the performance of the models. By adapting the proposed method to these domains, the models can effectively handle the diverse semantic content and relationships present in the data.