Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach

TL;DR

This paper introduces CLUSTER, a hierarchical Bayesian nonparametric model that identifies user transfer patterns in industrial resource management, enabling accurate predictions and uncertainty quantification without compromising privacy.

Contribution

The paper presents a novel interpretable Bayesian model that automates user cluster detection and predicts transfer patterns amid resource variation, advancing resource management strategies.

Findings

High alignment between predictions and empirical data

Effective uncertainty quantification for decision-making

Model preserves user privacy by not using personal data

Abstract

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.