用于多层次产品类别预测的文本分类
Text Classification for Predicting Multi-level Product Categories
摘要：
在网上购物平台中，产品的详细分类便于用户导航。它还可以帮助在线零售商跟踪特定行业的价格波动或特定产品类别的特别折扣。此外，自动分类系统可有助于查明操作员建议的不正确或主观类别。在本研究中，我们专注于杂货产品的产品名称分类。我们对六种不同的文本分类模型进行了全面的比较，为这项任务建立了一个强大的基线，这涉及到测试传统和最近的机器学习方法。在我们的实验中，我们研究了训练模型对其他在线零售商产品的可推广性，对预训练语言模型的不可行子类别的动态掩蔽，以及将产品标题纳入多种语言的好处。我们的数值结果表明，动态掩蔽的子类别是有效的，在提高预测精度。此外，我们观察到使用双语产品标题通常是有益的，基于神经网络的模型的性能明显优于SVM和XGBoost模型。最后，我们调查了错误分类产品的原因，并提出了未来的研究方向，以进一步提高预测模型。
英文摘要：
In an online shopping platform, a detailed classification of the products facilitates user navigation. It also helps online retailers keep track of the price fluctuations in a certain industry or special discounts on a specific product category. Moreover, an automated classification system may help to pinpoint incorrect or subjective categories suggested by an operator. In this study, we focus on product title classification of the grocery products. We perform a comprehensive comparison of six different text classification models to establish a strong baseline for this task, which involves testing both traditional and recent machine learning methods. In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages. Our numerical results indicate that dynamic masking of subcategories is effective in improving prediction accuracy. In addition, we observe that using bilingual product titles is generally beneficial, and neural network-based models perform significantly better than SVM and XGBoost models. Lastly, we investigate the reasons for the misclassified products and propose future research directions to further enhance the prediction models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
