A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews

DC FieldValueLanguage
dc.contributor.authorMuhammad, Khalil-
dc.contributor.authorLawlor, Aonghus-
dc.contributor.authorSmyth, Barry-
dc.date.accessioned2019-04-24T12:05:34Z-
dc.date.available2019-04-24T12:05:34Z-
dc.date.copyright2018 Association for the Advancement of Artificial Intelligenceen_US
dc.date.issued2018-05-23-
dc.identifier.urihttp://hdl.handle.net/10197/10126-
dc.descriptionThe Thirty-First International FLAIRS Conference (FLAIRS-31), Florida, United States of America, 21-23 May 2018en_US
dc.description.abstractThis paper extends recent work on the use of explanations in recommender systems. In particular, we show how explanations can be used to rank as well as justify recommendations, then we compare the results to more conventional recommendation approaches, in three large-scale application domains.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherAAAI Publicationsen_US
dc.subjectExplanationsen_US
dc.subjectRecommender systemsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectExplanation-based recommendationen_US
dc.titleA Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviewsen_US
dc.typeConference Publicationen_US
dc.internal.webversionshttps://sites.google.com/site/flairs31conference/-
dc.internal.webversionshttps://www.aaai.org/ocs/index.php/AAAI/FLAIRS18/index-
dc.statusPeer revieweden_US
dc.neeo.contributorMuhammad|Khalil|aut|-
dc.neeo.contributorLawlor|Aonghus|aut|-
dc.neeo.contributorSmyth|Barry|aut|-
dc.date.updated2018-08-23T08:20:37Z-
dc.identifier.grantidSFI/12/RC/2289-
item.fulltextWith Fulltext-
item.grantfulltextopen-
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