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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorAsma, Belhadi
dc.contributor.authorChun-Wei Lin, Jerry
dc.contributor.authorAlberto, Cano
dc.date.accessioned2023-08-21T09:02:30Z
dc.date.available2023-08-21T09:02:30Z
dc.date.created2020-05-12T15:02:02Z
dc.date.issued2020
dc.identifier.citationIEEE Access. 2020, 8, 79182-79191.en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/3084986
dc.description.abstractThis paper addresses the hashtag recommendation problem using high average-utility pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for Hashtag Recommendation). It consists of two main stages. First, offline processing transforms the corpus of tweets into a transactional database considering the temporal information of the tagged tweets (tweets with hashtags). The method discovers the temporal top k high average utility patterns. Irrelevant tagged tweets and the ontology of tagged tweets are also constructed offline. Second, an online processing inputs the utility patterns, the ontology, and the irrelevant tagged tweets to extract the most relevant hashtags for a given orpheline tweet (tweet without hashtags). Extensive experiments were carried out on large tweets collections. The proposed PM-HRec outperforms the existing state of the art hashtag recommendation approaches in terms of quality of recommended hashtags and runtime processing.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Data-Driven Approach for Twitter Hashtag Recommendationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber79182-79191en_US
dc.source.volume8en_US
dc.source.journalIEEE Accessen_US
dc.identifier.doi10.1109/ACCESS.2020.2990799
dc.identifier.cristin1810565
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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