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- W4200268908 abstract "Today’s E-commerce is hot, while the categorization of goods cannot be handled better, especially to achieve the demand of multiple tasks. In this paper, we propose a multitask learning model based on a CNN in parallel with a BiLSTM optimized by an attention mechanism as a training network for E-commerce. The results showed that the fast classification task of E-commerce was performed using only 10% of the total number of products. The experimental results show that the accuracy of w-item2vec for product classification can be close to 50% with only 10% of the training data. Both models significantly outperform other models in terms of classification accuracy." @default.
- W4200268908 created "2021-12-31" @default.
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- W4200268908 date "2021-12-15" @default.
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- W4200268908 title "Rapid Classification and Analysis for E-Commerce Goods Based on Multitask Learning" @default.
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- W4200268908 doi "https://doi.org/10.1155/2021/1725544" @default.
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