In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development.

翻译：在众多工业领域中，一个关键目标是在满足用户需求的同时优化资源管理。工业从业者的资源管理可能导致用户负载在资源提供者之间的被动转移，准确刻画这一现象既具挑战性又至关重要。本研究揭示了用户聚类的存在，这些聚类能够捕捉资源变化下宏观层面的用户转移模式。我们随后提出了CLUSTER——一种可解释的分层贝叶斯非参数模型，可自动识别聚类，从而预测用户对资源变化的响应行为。此外，CLUSTER通过支持不确定性量化，为后续可靠决策提供保障。该方法无需依赖个人身份信息即可运行，实现了隐私保护。基于通信行业仿真数据与真实数据的实验表明，在多种资源管理场景下，预测结果与实证观测之间呈现出显著的一致性。本研究为推进资源管理策略开发奠定了坚实基础。