论文标题
通过NLP提高电子商务的产品安全性
Enhancing Product Safety in E-Commerce with NLP
论文作者
论文摘要
确保向客户提供的产品安全至关重要。尽管对这些平台上列出的产品进行了严格的质量和安全检查，但有时客户可能会收到可能因其使用而引起的安全问题。在本文中，我们介绍了一种创新的机制，即大规模跨国电子商务平台Zalando如何使用自然语言处理技术来帮助及时调查直接从非结构化纯文本的客户书面索赔中直接从客户书面索赔中开采的潜在不安全产品。我们系统地描述了与Zalando客户有关的安全问题的类型。我们演示了如何将这个核心业务问题映射到有监督的文本分类问题中，并在AI-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-INS设置中进行了映射，重点是关注关键性能指标（KPI）驱动的评估。最后，我们提出了详细的消融研究，以显示不同分类技术之间的全面比较。我们以这种NLP模型的部署方式结束了工作。
Ensuring safety of the products offered to the customers is of paramount importance to any e- commerce platform. Despite stringent quality and safety checking of products listed on these platforms, occasionally customers might receive a product that can pose a safety issue arising out of its use. In this paper, we present an innovative mechanism of how a large scale multinational e-commerce platform, Zalando, uses Natural Language Processing techniques to assist timely investigation of the potentially unsafe products mined directly from customer written claims in unstructured plain text. We systematically describe the types of safety issues that concern Zalando customers. We demonstrate how we map this core business problem into a supervised text classification problem with highly imbalanced, noisy, multilingual data in a AI-in-the-loop setup with a focus on Key Performance Indicator (KPI) driven evaluation. Finally, we present detailed ablation studies to show a comprehensive comparison between different classification techniques. We conclude the work with how this NLP model was deployed.
