Generative models for ticket resolution in expert networks

  • Authors:

  • Gengxin Miao;Louise E. Moser;Xifeng Yan;Shu Tao;Yi Chen;Nikos Anerousis

  • Affiliations:

  • University of California at Santa Barbara, Santa Barbara, CA, USA;University of California at Santa Barbara, Santa Barbara, CA, USA;University of California at Santa Barbara, Santa Barbara, CA, USA;IBM T. J. Watson, Hawthorne, NY, USA;Arizona State University, Tempe, AZ, USA;IBM T. J. Watson, Hawthorne, NY, USA

  • Venue:
  • Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2010

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Abstract

Ticket resolution is a critical, yet challenging, 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 right expertise to solve the problem. Finding a methodology that reduces such bouncing and hence shortens ticket resolution time is a long-standing challenge. In this paper, 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 represent 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 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.