## 6 Conclusions  
In summary, based on the introduction and implementation of the C-MBR model, this study has successfully addressed its initial research objective of focusing on the dynamics of user behavioral preferences to tackle the challenges of data sparsity and cold start problems in constructing e-trans infrastructure, especially for charging networks. Consequently, there has been a significant improvement in one of the key components aimed at maintaining a sustainable e-trans infrastructure to enhance resilience in the face of disruptive conditions. Through experimental research, it was found that C-MBR has a strong recommendation value for studying users' dynamic preferences. In the process of dataset analysis, based on the performance possessed by the HR evaluation index, it can be found that the C-MBR model finally presents data results of higher quality. In the analysis of ablation experiments, it can be found that the C-MBR model has strong comprehensiveness and can effectively improve the quality of comparison. Exploring the analysis of the C-MBR model for the cold start problem, it is found that this model can alleviate the data sparsity problem. Finally, it is shown that the C-MBR proposed in this paper is effective on public datasets. A notable limitation lies in the reliance on simulated or synthetic data for assessing the model efficacy, which may not fully encapsulate the intricacies of real-world e-trans dynamics. To mitigate this constraint, forthcoming research could concentrate on amassing and scrutinizing extensive real-world datasets to authenticate the models' efficacy in authentic settings. Furthermore, while the evaluation metrics employed in this study, such as accuracy, coverage, and diversity, are widely acknowledged, they might not comprehensively encompass the resilience and sustainability attributes of e-trans infrastructure. Future investigations could formulate novel assessment metrics that explicitly consider resilience and sustainability factors, such as adaptability to market fluctuations, resilience to disruptions, and environmental impact. Moreover, the research predominantly focuses on the technical dimensions of model performance, overlooking broader socioeconomic and organizational factors influencing the maintenance of enduring e-trans infrastructure. Subsequent studies could adopt an interdisciplinary approach, amalgamating insights from domains like business management, economics, and environmental science to formulate holistic frameworks for the assessment and fortification of e-trans infrastructure sustainability. In essence, addressing these limitations and pursuing interdisciplinary research trajectories will enrich our comprehension of effectively upholding sustainable e-trans infrastructure and bolstering resilience amidst burgeoning challenges. From an algorithmic perspective, the cross-behavioral comparative and sequence-based cross-behavioral aspects of the training model have some limitations. Integrating cross-behavioral aspects into the model increases its complexity, which can lead to longer training times and higher computational costs. The interpretation of results becomes more challenging as the model becomes more complex due to the inclusion of cross-behavioral aspects. The effectiveness of cross-behavioral aspects may vary depending on the specific context and dataset used for training. As for applications in other fields, difficulties may arise in adapting cross-behavioral aspects to different domains due to the following reasons. First, behavior patterns and interactions may differ significantly across domains. Data for training models with cross-behavioral aspects may be more difficult in some domains where relevant data are scarce or less accessible. Certain domains may have unique constraints or considerations that affect the applicability of cross-behavioral aspects. Future research may focus on improving training time as well as reducing the complexity of algorithm interpretation.