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arXiv · 2005.06692

Deep Hierarchical Classification for Category Prediction in E-commerce System

Dehong Gao·Wenjing Yang·Huiling Zhou·Yi Wei·Yi Hu·Hao Wang
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Abstract

In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy.

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Dehong Gao, Wenjing Yang, Huiling Zhou, Yi Wei, Yi Hu, Hao Wang. 2020-05-14. Deep Hierarchical Classification for Category Prediction in E-commerce System. https://arxiv.org/abs/2005.06692

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