  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.
