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A multi-agent recommender system

dc.contributor.authorMorais, A. Jorge
dc.contributor.authorOliveira, Eugénio
dc.contributor.authorJorge, Alípio Mário
dc.date.accessioned2017-01-24T13:27:38Z
dc.date.available2017-01-24T13:27:38Z
dc.date.issued2012
dc.description.abstractThe large amount of pages in Websites is a problem for users who waste time looking for the information they really want. Knowledge about users’ previous visits may provide patterns that allow the customization of the Website. This concept is known as Adaptive Website: a Website that adapts itself for the purpose of improving the user’s experience. Some Web Mining algorithms have been proposed for adapting a Website. In this paper, a recommender system using agents with two different algorithms (associative rules and collaborative filtering) is described. Both algorithms are incremental and work with binary data. Results show that this multi-agent approach combining different algorithms is capable of improving user’s satisfaction.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-642-28765-7_33pt_PT
dc.identifier.isbn978-3-642-28764-0 (Print)
dc.identifier.isbn978-3-642-28765-7 (Online)
dc.identifier.urihttp://hdl.handle.net/10400.2/5973
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-642-28765-7_33pt_PT
dc.titleA multi-agent recommender systempt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage288pt_PT
oaire.citation.startPage281pt_PT
oaire.citation.titleDistributed Computing and Artificial Intelligencept_PT
oaire.citation.volume151pt_PT
person.familyNameMorais
person.givenNameA. Jorge
person.identifierD-1723-2009
person.identifier.ciencia-idF314-1D77-536E
person.identifier.orcid0000-0003-2224-1609
person.identifier.scopus-author-id57194584599
rcaap.rightsrestrictedAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublication571a1c49-329b-4b4e-ad48-78c5ff9c6e01
relation.isAuthorOfPublication.latestForDiscovery571a1c49-329b-4b4e-ad48-78c5ff9c6e01

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