Abstract
Existing e-commerce platforms heavily rely on manual annotation for product categorization, which is inefficient and inconsistent. These platforms often employ a hierarchical structure for categorizing products; however, few studies have leveraged this hierarchical information for classification. Furthermore, studies that consider hierarchical information fail to account for similarities and differences across various hierarchical categories. Herein, we introduce a large-scale hierarchical dataset collected from the JD e-commerce platform (this http URL), comprising 1,011,450 products with titles and a three-level category structure. By making this dataset openly accessible, we provide a valuable resource for researchers and practitioners to advance research and applications associated with product categorization. Moreover, we propose a novel hierarchical text classification approach based on the widely used Bidirectional Encoder Representations from Transformers (BERT), called Hierarchical Fine-tuning BERT (HFT-BERT). HFT-BERT leverages the remarkable text feature extraction capabilities of BERT, achieving prediction performance comparable to those of existing methods on short texts. Notably, our HFT-BERT model demonstrates exceptional performance in categorizing longer short texts, such as books.
Abstract (translated)
现有的电子商务平台在商品分类时严重依赖于人工标注，这不仅效率低下而且一致性差。这些平台通常采用分层结构对产品进行分类；然而，鲜有研究利用这种层级信息来进行分类。更重要的是，考虑到层级信息的研究未能充分考虑不同层级类别之间的相似性和差异性。 在此背景下，我们引入了一个大规模的分层数据集，该数据集从京东电子商务平台收集而来（此链接），包含1,011,450个具有标题和三级类别的产品。通过公开提供这个数据集，我们为研究者和从业者提供了推进与商品分类相关的研究和应用的重要资源。 此外，我们提出了一种基于广泛使用的双向编码表示从变压器(BERT)的新型分层文本分类方法，称为层次微调BERT（HFT-BERT）。HFT-BERT利用了BERT卓越的文本特征提取能力，在短文本上的预测性能可与现有方法媲美。值得注意的是，我们的HFT-BERT模型在对较长的短文本进行分类时表现出色，例如书籍类商品。 这个研究为电子商务领域的商品自动分类提供了新的视角和工具，并且通过提供一个大规模、结构化的数据集以及一个改进的分类框架，有望提高整个行业的效率和准确性。
URL
https://arxiv.org/abs/2508.15800
