Deep Hierarchical Classification for Category Prediction in E-commerce System

• URL: http://arxiv.org/abs/2005.06692v1
• Date: Thu, 14 May 2020 02:29:14 GMT
• Title: Deep Hierarchical Classification for Category Prediction in E-commerce System
• Authors: Dehong Gao, Wenjing Yang, Huiling Zhou, Yi Wei, Yi Hu and Hao Wang
• Abstract summary: In e-commerce system, category prediction is to automatically predict categories of given texts. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks. We also define a novel combined loss function to punish hierarchical prediction losses.
• Score: 16.6932395109085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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