Partially Observable Markov Decision Process Modelling for Assessing Hierarchies
This addresses the lack of agreed evaluation methods for hierarchies, particularly useful for applications like online retailer product catalogues, but is incremental as it builds on decision-theoretic perspectives.
The paper tackles the problem of evaluating hierarchical clustering without ground-truth labels by proposing a framework that models a bot searching for items in the hierarchy, using Partially Observable Markov Decision Processes (POMDP) to measure search support.
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.