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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorBelhadi, Asma
dc.contributor.authorSrivastava, Gautam
dc.contributor.authorLin, Jerry Chun-Wei
dc.date.accessioned2023-02-22T14:56:41Z
dc.date.available2023-02-22T14:56:41Z
dc.date.created2022-09-07T14:51:08Z
dc.date.issued2022
dc.identifier.citationInformation Sciences. 2022, 609, 1506-1517.en_US
dc.identifier.issn0020-0255
dc.identifier.urihttps://hdl.handle.net/11250/3053399
dc.description.abstractThis work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectdeep learningen_US
dc.subjectsocial mediaen_US
dc.subjectpattern recommendationen_US
dc.subjecttweet dataen_US
dc.subjecttweetingen_US
dc.subjecttvitringen_US
dc.subjecttwitteren_US
dc.subjectdyplæringen_US
dc.subjectsosiale medieren_US
dc.titleDeep learning based hashtag recommendation system for multimedia dataen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1506-1517en_US
dc.source.volume609en_US
dc.source.journalInformation Sciencesen_US
dc.identifier.doi10.1016/j.ins.2022.07.132
dc.identifier.cristin2049575
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal