Universal Model in Online Customer Service
Shu-Ting Pi Cheng-Ping Hsieh Qun Liu Yuying Zhu
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.