DC FieldValueLanguage
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorRaftopoulos, Konstantinos A.-
dc.contributor.authorPapadakis, Nikos-
dc.contributor.authorKollias, Stefanos-
dc.date.accessioned2024-11-19T11:40:23Z-
dc.date.available2024-11-19T11:40:23Z-
dc.date.issued2007-12-01-
dc.identifierscopus-47749114592-
dc.identifier.isbn0-7695-2997-6-
dc.identifier.other47749114592-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2915-
dc.description.abstractWe use a Markovian model to capture the habitual user profiles of an information access system. In this model, the general as well as the individual for each user, profile is captured in the form of a Markovian process where the states are the keywords asked to the system by the users and a transition from state to state corresponds to the order theses keywords appeared in the queries. Under this model the probabilistic locality of the Markovian state space translates to semantical locality of the corresponding keywords in a way that a clustering of the Markovian state space corresponds to a semantic clustering of the keyword space. Since the states represent keywords asked by the users, the state space can grow very large, but at the same time it is partitioned into disjoint subsets such that strong interactions among the states of the same subset exists but weak interactions among states of different subsets. We exploit this structure to effectively cluster the large state space and reveal the corresponding semantic keyword clusters. We then define a semantic distance between the various user profiles that can be used to cluster the user space on the basis of keyword usage and keyword semantic relevance. The resulting clustering achieves high independence from the row data. Users for e.g. that never asked a common keyword may end up very close to each other if their keywords were asked together by many other users.en_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the International Conference on Semantic Computing (ICSC 2007)en_US
dc.subjectMarkovian state clusteringen_US
dc.subjectSemantic clusteringen_US
dc.subjectUser profile clusteringen_US
dc.titleSemantic clustering of information systems' users with stochastic techniquesen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Semantic Computing (ICSC 2007), 17-19 September 2007, California, USAen_US
dc.identifier.doi10.1109/ICSC.2007.27en_US
dc.identifier.scopus2-s2.0-47749114592-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage535en_US
dc.identifier.epage542en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusnot verifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
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