Multimodal classification for detecting products that do not comply with the Americanas S.A.'s marketplace sales policies
Resumen
Multimodal learning for the e-commerce domain, some classification methods are ne-
eded for categorization, information retrieval and product recommendations, which are
generally composed of different modalities: images and texts. Due to large diversification
in the characteristics of these modalities or the absence/incompleteness of information (for
example, incomplete product attributes), classification methods face many difficulties in
dealing with this information in order to improve their classification. Thus, this work
was carried out to investigate the multimodal learning in visual and textual modalities
for e-commerce. Our experiments show good results for classification of products from
“Adult” and “Illegal Devices” categories, which is part of the dataset provided by the
partner company of this project. In these experiments, training was carried out for the
specific modalities, deriving text and image models, as well as the fusion of the two mo-
dalities in a multimodal model. The best models were the binary textual models trained
taking into account product titles and descriptions: TD bin-adult (with a recall of 98%)
and TD bin-illegal (with a recall of 95 %). We have some insights about the multimo-
dal classification, mainly for the visual modality which, regarding its nature, could not
capture patterns as well as textual models.
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