Machine Learning Based Personalized Customer Service in E-commerce Trading

International trade is becoming more and more prosperous, and among the international trade, electronic commerce accounts for an increasing proportion. Hence, the competition among different e-commerce trading platforms is becoming increasingly fierce. Based on different customers, providing personalized service can make the trading platform stand out among other trading platforms. Therefore, how to provide personalized customer service becomes an important problem that needs to be solved. Given an international online transaction dataset, machine learning gives a possibility to solve this problem. Customers are clustered into different clusters based on the k-means model. In addition, strong association rules among commodities are generated according to the apriori model by introducing support and confidence metrics. The experimental results demonstrate that both results together provide a solution to provide personalized services for the customers.