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Navigating e-commerce product taxonomy challenges and label ambiguities trough self- and unsupervised learning

datacite.subject.fosCiências Sociais::Economia e Gestãopt_PT
dc.contributor.advisorHan, Qiwei
dc.contributor.authorJustus, Adrien Robert
dc.date.accessioned2025-01-03T09:56:36Z
dc.date.available2025-01-03T09:56:36Z
dc.date.issued2024-01-19
dc.date.submitted2024-01-19
dc.description.abstractUsing state-of-the-art models for text (BERT-based) and image (ResNet, VGG16, ViT) analysis, this study develops a multimodal approach that leverages the collective strengths of both domains. Our results show that the combination of knowledge from text and image domains leads to the best classification framework, which achieves a remarkable macro-F1 score of 98% at all levels. This innovative approach significantly improves classification accuracy and efficiency in e-commerce. In addition, the study explores self-supervised learning and introduces a detailed taxonomy that provides comprehensive insights. This research highlights the superiority of a synergistic multimodal strategy to improve product understanding.pt_PT
dc.identifier.tid203681916pt_PT
dc.identifier.urihttp://hdl.handle.net/10362/176971
dc.language.isoengpt_PT
dc.relationUID/ECO/00124/2013pt_PT
dc.subjectSelf supervised learningpt_PT
dc.subjectUnsupervised learningpt_PT
dc.subjectTaxonomypt_PT
dc.subjectSimclrpt_PT
dc.subjectHierarchical clusteringpt_PT
dc.titleNavigating e-commerce product taxonomy challenges and label ambiguities trough self- and unsupervised learningpt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameA Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economicspt_PT

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