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dc.contributor.authorTerziyan, Vagan
dc.contributor.authorGolovianko, Mariia
dc.contributor.authorCochez, Michael
dc.contributor.editorCalì, Andrea
dc.contributor.editorGorgan, Dorian
dc.contributor.editorUgarte, Martín
dc.date.accessioned2017-11-17T12:24:28Z
dc.date.available2017-11-17T12:24:28Z
dc.date.issued2017
dc.identifier.citationTerziyan, V., Golovianko, M., & Cochez, M. (2017). TB-Structure : Collective Intelligence for Exploratory Keyword Search. In A. Calì, D. Gorgan, & M. Ugarte (Eds.), <i>Semantic Keyword-Based Search on Structured Data Sources. COST Action IC1302 Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8–9, 2016, Revised Selected Papers</i> (pp. 171-178). Springer International Publishing. Lecture Notes in Computer Science, 10151. <a href="https://doi.org/10.1007/978-3-319-53640-8_15" target="_blank">https://doi.org/10.1007/978-3-319-53640-8_15</a>
dc.identifier.otherCONVID_26550673
dc.identifier.otherTUTKAID_72998
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55916
dc.description.abstractIn this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s intents. We used experiments to demonstrate both: the storage compactness and inference potential of the proposed structure.
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofSemantic Keyword-Based Search on Structured Data Sources. COST Action IC1302 Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8–9, 2016, Revised Selected Papers
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.otherkeyword search
dc.subject.otherquery trail
dc.subject.otherTB-structure
dc.titleTB-Structure : Collective Intelligence for Exploratory Keyword Search
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201711164273
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-11-16T13:28:07Z
dc.relation.isbn978-3-319-53639-2
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange171-178
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Keystone conference
dc.subject.ysojoukkoäly
jyx.subject.urihttp://www.yso.fi/onto/yso/p24770
dc.relation.doi10.1007/978-3-319-53640-8_15
dc.type.okmA4


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