Deep Hierarchical Classification for Category Prediction in E-commerce System
This addresses category prediction for e-commerce systems, but it is incremental as it builds on existing hierarchical classification methods.
The paper tackles hierarchical category prediction in e-commerce by proposing a Deep Hierarchical Classification framework that incorporates multi-scale hierarchical information and a representation sharing strategy, resulting in improved accuracy over existing approaches.
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.