Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Partially Observable Markov Decision Process Modelling for Assessing
  Hierarchies   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.

11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th