Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/13248
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dc.contributor.authorTurchi, T-
dc.contributor.authorMalizia, A-
dc.contributor.authorCastellucci, P-
dc.contributor.authorOlsen, K-
dc.date.accessioned2016-09-29T11:47:19Z-
dc.date.available2016-01-01-
dc.date.available2016-09-29T11:47:19Z-
dc.date.issued2016-
dc.identifier.citationCommunications in Computer and Information Science, 612: pp. 104 - 115, (2016)en_US
dc.identifier.isbn9783319419374-
dc.identifier.issn1865-0929-
dc.identifier.urihttp://link.springer.com/chapter/10.1007%2F978-3-319-41938-1_12-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/13248-
dc.description.abstractIn this paper we propose a new ranking algorithm based on Swarm Intelligence, more specifically on the Ant Colony Optimization technique, to improve search engines’ performances and reduce the information overload by exploiting users’ collective behavior. We designed an online evaluation involving end users to test our algorithm in a real-world scenario dealing with informational queries. The development of a fully working prototype – based on the Wikipedia search engine – demonstrated promising preliminary results.en_US
dc.format.extent104 - 115-
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.subjectSwarm Intelligenceen_US
dc.subjectAnt Colony Optimization techniqueen_US
dc.subjectRanking algorithmen_US
dc.titleCollaborative information seeking with ant colony ranking in real-timeen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-41938-1_12-
dc.relation.isPartOfCommunications in Computer and Information Science-
pubs.publication-statusPublished-
pubs.volume612-
Appears in Collections:Dept of Computer Science Research Papers

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