Extreme Multi-label Classification with Hierarchical Multi-task for Product Attribute Identification

Jun Zhang, Menqian Cai, Chenyu Zhao, Xiaowei Zhang, Zhiqian Zhang, Haiheng Chen, Sulong Xu

Abstract: Identification of product attributes (product type, brand, color, gender, etc.) from a query is critically important for e-commerce search systems, especially the identification of brand intent. Recently, Named Entity Recognition (NER) method has been used to address this issue. However, the limitation of NER method is that it can only identify brand intent specified by terms of a query and cannot work appropriately if brand terms are not provided explicitly. To overcome this limitation, we propose a novel Extreme Multi-label based hierarchical Multi-tAsk (EMMA) framework, where we treat the brand identification as an issue of extreme multi-label classification; thereafter, a deep learning model is also developed to jointly learn query’s product intent and brand intent in a coarse-to-fine approach. The results from both online A/B test and offline experiment on real industrial dataset demonstrate the effectiveness of our proposed framework. Additionally, this framework may be extended potentially from e-commerce system to other search scenarios.