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dc.contributor.authorBelhadi, Asma
dc.contributor.authorDjenouri, Youcef
dc.contributor.authorAndrade, Fabio Augusto de Alcantara
dc.contributor.authorSrivastava, Gautam
dc.date.accessioned2024-08-13T08:36:16Z
dc.date.available2024-08-13T08:36:16Z
dc.date.created2024-05-21T09:40:17Z
dc.date.issued2024
dc.identifier.citationBelhadi, A., Djenouri, Y., de Alcantara Andrade, F. A., & Srivastava, G. (2024). Federated Constrastive Learning and Visual Transformers for Personal Recommendation. Cognitive Computation, 16(5), 2551-2565.en_US
dc.identifier.issn1866-9956
dc.identifier.urihttps://hdl.handle.net/11250/3145953
dc.description.abstractThis paper introduces a novel solution for personal recommendation in consumer electronic applications. It addresses, on the one hand, the data confidentiality during the training, by exploring federated learning and trusted authority mechanisms. On the other hand, it deals with data quantity, and quality by exploring both transformers and consumer clustering. The process starts by clustering the consumers into similar clusters using contrastive learning and k-means algorithm. The local model of each consumer is trained on the local data. The local models of the consumers with the clustering information are then sent to the server, where integrity verification is performed by a trusted authority. Instead of traditional federated learning solutions, two kinds of aggregation are performed. The first one is the aggregation of all models of the consumers to derive the global model. The second one is the aggregation of the models of each cluster to derive a local model of similar consumers. Both models are sent to the consumers, where each consumer decides which appropriate model might be used for personal recommendation. Robust experiments have been carried out to demonstrate the applicability of the method using MovieLens-1M, and Amazon-book. The results reveal the superiority of the proposed method compared to the baseline methods, where it reaches an average accuracy of 0.27, against the other methods that do not exceed 0.25.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleFederated Constrastive Learning and Visual Transformers for Personal Recommendationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© The Author(s) 2024.en_US
dc.source.pagenumber2551-2565en_US
dc.source.volume16en_US
dc.source.journalCognitive Computationen_US
dc.source.issue5en_US
dc.identifier.doihttps://doi.org/10.1007/s12559-024-10286-0
dc.identifier.cristin2269598
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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