Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance and account for variations in data distribution, regular retraining is necessary. This paper introduces a solution for improving online customer service in e-commerce by presenting a universal model for predict-ing labels based on customer questions, without requiring training. Our novel approach involves using machine learning techniques to tag customer questions in transcripts and create a repository of questions and corresponding labels. When a customer requests assistance, an information retrieval model searches the repository for similar questions, and statistical analysis is used to predict the corresponding label. By eliminating the need for individual model training and maintenance, our approach reduces both the model development cycle and costs. The repository only requires periodic updating to maintain accuracy.

翻译：构建机器学习模型通常是一个耗时的过程，在典型业务场景中往往需要数月时间才能实现。为确保模型性能的稳定性并应对数据分布的变化，定期重新训练是必要的。本文提出了一种改进电商在线客服的解决方案，通过引入一个无需训练的通用模型，基于客户问题预测标签。我们的新颖方法利用机器学习技术对对话记录中的客户问题进行标注，并建立一个包含问题及对应标签的存储库。当客户请求协助时，信息检索模型在存储库中搜索相似问题，并通过统计分析预测对应标签。由于无需针对每个模型进行单独训练与维护，该方法同时缩短了模型开发周期并降低了成本。该存储库仅需定期更新以保持准确性。