Problem ticket resolution is an important aspect of the delivery of IT services. A large service provider needs to handle, on a daily basis, thousands of tickets that report various types of problems. Many of those tickets bounce among multiple expert groups before being transferred to the group with the expertise to solve the problem. Finding a methodology that can automatically make reliable ticket routing decisions and that reduces such bouncing and, hence, shortens ticket resolution time is a long-standing challenge. Reliable ticket routing forwards the ticket to an expert who either can solve the problem reported in the ticket, or can reach an expert who can resolve the ticket. In this chapter, we present a unified generative model, the Optimized Network Model (ONM), that characterizes the lifecycle of a ticket, using both the content and the routing sequence of the ticket. ONM uses maximum likelihood estimation to capture reliable ticket transfer profiles on each edge of an expert network. These transfer profiles reflect how the information contained in a ticket is used by human experts to make ticket routing decisions. Based on ONM, we develop a probabilistic algorithm to generate reliable ticket routing recommendations for new tickets in a network of expert groups. Our algorithm calculates all possible routes to potential resolvers and makes globally optimal recommendations, in contrast to existing classification methods that make static and locally optimal recommendations. Experiments show that our method significantly outperforms existing solutions and, hence, yields better performance in real-world applications.
All Science Journal Classification (ASJC) codes
- Computer Science(all)