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dc.contributor.editorEllen M. Voorhees and Angela Ellisen
dc.date.accessioned2018-08-23T14:44:52Z
dc.date.available2018-08-23T14:44:52Z
dc.date.createdNovember 15--18en
dc.date.issued2017-11
dc.date.submitted2017en
dc.identifier.citationMostafa Bayomi, S?amus Lawless, ADAPT_TCD: An Ontology-Based Context Aware Approach for Contextual Suggestion, Twenty-Fifth Text REtrieval Conference (TREC 2016), Gaithersburg, Maryland, United States, November 15--18, Ellen M. Voorhees and Angela Ellis, National Institute for Standards and Technology (NIST), 2017en
dc.identifier.otherY
dc.description.abstractIn this paper we give an overview of the participation of the ADAPT Centre, Trinity College Dublin, Ireland, in both phases of the TREC 2016 Contextual Suggestion Track. We present our ontology-based approach that consists of three models that are based on an ontology that was extracted from the Foursquare category hierarchy. The three models are: User Model, Document Model and Rule Model. In the User Model we build two models, one for each phase of the task, based upon the attractions that were rated in the user’s profile. The Document Model enriches documents with extra metadata from Foursquare and categories (concepts) from the ontology are attached to each document. The Rule model is used to tune the score for candidate suggestions based on how the context of the trip aligns with the rules in the model. The results of our submitted runs, in both phases, demonstrate the effectiveness of the proposed methods.en
dc.description.sponsorshipThe ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.en
dc.language.isoenen
dc.publisherNational Institute for Standards and Technology (NIST)en
dc.relation.urihttp://trec.nist.gov/pubs/trec25/papers/Overview-CS.pdfen
dc.rightsYen
dc.titleADAPT_TCD: An Ontology-Based Context Aware Approach for Contextual Suggestionen
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeCreative Technologiesen
dc.subject.TCDThemeIntelligent Content & Communicationsen
dc.subject.TCDTagINFORMATION-RETRIEVALen
dc.subject.TCDTagOntologyen
dc.subject.TCDTagRECOMMENDER SYSTEMSen
dc.relation.iscitedbyIsCitedByen
dc.status.accessibleNen
dc.identifier.urihttp://hdl.handle.net/2262/84052


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