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Multi-agent web recommendations

dc.contributor.authorNeto, Joaquim
dc.contributor.authorMorais, A. Jorge
dc.date.accessioned2017-01-24T14:50:55Z
dc.date.available2017-01-24T14:50:55Z
dc.date.issued2014
dc.description.abstractDue to the large amount of pages in Websites it is important to collect knowledge about users’ previous visits in order to provide patterns that allow the customization of the Website. In previous work we proposed a multi-agent approach using agents with two different algorithms (associative rules and collaborative filtering) and showed the results of the offline tests. Both algorithms are incremental and work with binary data. In this paper we present the results of experiments held online. 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.doi10.1007/978-3-319-07593-8_28pt_PT
dc.identifier.isbn978-3-319-07592-1 (Print)
dc.identifier.isbn978-3-319-07593-8 (Online)
dc.identifier.urihttp://hdl.handle.net/10400.2/5975
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.titleMulti-agent web recommendationspt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage242pt_PT
oaire.citation.startPage235pt_PT
oaire.citation.titleDistributed Computing and Artificial Intelligence, 11th International Conferencept_PT
oaire.citation.volume290pt_PT
person.familyNameNeto
person.familyNameMorais
person.givenNameJoaquim
person.givenNameA. Jorge
person.identifier2488644
person.identifierD-1723-2009
person.identifier.ciencia-id0A10-2459-C7E6
person.identifier.ciencia-idF314-1D77-536E
person.identifier.orcid0000-0003-1228-1236
person.identifier.orcid0000-0003-2224-1609
person.identifier.ridHKO-1960-2023
person.identifier.scopus-author-id56326334100
person.identifier.scopus-author-id57194584599
rcaap.rightsrestrictedAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublication809f9cc1-ef9c-4d9f-8bb9-a1cb572a1c78
relation.isAuthorOfPublication571a1c49-329b-4b4e-ad48-78c5ff9c6e01
relation.isAuthorOfPublication.latestForDiscovery571a1c49-329b-4b4e-ad48-78c5ff9c6e01

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