Evaluation of Hierarchical Clustering via Markov Decision Processes for Efficient Navigation and Search

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Title: Evaluation of Hierarchical Clustering via Markov Decision Processes for Efficient Navigation and Search
Authors: Moreno, RaulHuáng, WěipéngYounus, ArjumandO'Mahony, Michael P.Hurley, Neil J.
Permanent link: http://hdl.handle.net/10197/9169
Date: 14-Sep-2017
Online since: 2018-01-15T13:23:03Z
Abstract: In this paper, we propose a new evaluation measure to assessthe quality of a hierarchy in supporting search queries to content collections.The evaluation measure models the scenario of a searcher seeking a particular target item in the hierarchy. It takes into account the structureof the hierarchy by measuring the cognitive challenge of determiningthe correct path in the hierarchy as well as the reduction in search timeaorded by hierarchy. The goal is to propose a general-purpose measurethat can be applied in dierent application contexts, allowing dierenthierarchical arrangements of content to be quantitatively assessed
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science book series (LNCS, volume 10456)
Copyright (published version): 2017 Springer
Keywords: Markov decision processesHierarchy navigation
DOI: 10.1007/978-3-319-65813-1_12
Language: en
Status of Item: Peer reviewed
Conference Details: 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14 2017
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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