论文标题
电子商务系统类别预测的深层分级分类
Deep Hierarchical Classification for Category Prediction in E-commerce System
论文作者
论文摘要
在电子商务系统中，类别预测是自动预测给定文本的类别。与类别之间没有关系的传统分类不同，类别预测被认为是标准的层次分类问题，因为类别通常被组织为层次树。在本文中，我们解决了层次类别的预测。我们提出了一个深层的层次分类框架，该框架结合了神经网络中的多尺度层次信息，并根据类别树引入了表示共享策略。我们还定义了一种新型的综合损失函数来惩罚分层预测损失。评估表明，所提出的方法在准确性上的表现优于现有方法。
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.
