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Navigating e-commerce product taxonomy challenges and label ambiguities trough self- and unsupervised learning
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Orientador(es)
Resumo(s)
Using 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.
Descrição
Palavras-chave
Self supervised learning Unsupervised learning Taxonomy Simclr Hierarchical clustering