Partially Observable Markov Decision Process Modelling for Assessing Hierarchies

TL;DR

This paper introduces a decision-theoretic framework using POMDPs to evaluate hierarchical clustering quality in scenarios lacking ground-truth labels, such as online product catalogues.

Contribution

It proposes a novel POMDP-based method for assessing hierarchy quality without ground-truth, addressing a gap in hierarchical clustering evaluation.

Findings

Framework effectively measures hierarchy support for search tasks

POMDP modeling captures uncertainty and decision-making in hierarchy evaluation

Applicable to real-world scenarios like online retail catalogues

Abstract

Hierarchical clustering has been shown to be valuable in many scenarios. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the hierarchies produced from different techniques, particularly in the case where ground-truth labels are unavailable. This motivates us to propose a framework for assessing the quality of hierarchical clustering allocations which covers the case of no ground-truth information. This measurement is useful, e.g., to assess the hierarchical structures used by online retailer websites to display their product catalogues. Our framework is one of the few attempts for the hierarchy evaluation from a decision-theoretic perspective. We model the process as a bot searching stochastically for items in the hierarchy and establish a measure representing the degree to which the hierarchy supports this search. We employ Partially Observable Markov Decision Processes (POMDP) to model the uncertainty, the decision making, and the cognitive return for searchers in such a scenario.