  Efficient and appropriate online customer service is essential to large
e-commerce businesses. Existing solution recommendation methods for online
customer service are unable to determine the best solutions at runtime, leading
to poor satisfaction of end customers. This paper proposes a novel intelligent
framework, called ICS-Assist, to recommend suitable customer service solutions
for service staff at runtime. Specifically, we develop a generalizable
two-stage machine learning model to identify customer service scenarios and
determine customer service solutions based on a scenario-solution mapping
table. We implement ICS-Assist and evaluate it using an over 6-month field
study with Alibaba Group. In our experiment, over 12,000 customer service staff
use ICS-Assist to serve for over 230,000 cases per day on average. The
experimen-tal results show that ICS-Assist significantly outperforms the
traditional manual method, and improves the solution acceptance rate, the
solution coverage rate, the average service time, the customer satisfaction
rate, and the business domain catering rate by up to 16%, 25%, 6%, 14% and 17%
respectively, compared to the state-of-the-art methods.
