Abstract
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.
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References
DBLP: http://www.informatik.uni-trier.de/∼ley/db/.
L. A. Adamic and E. Adar. How to search a social network. Social Networks, 27:2005, 2005.
J. Anvik, L. Hiew, and G. C. Murphy. Who should fix this bug? In Proceedings of the 28th International Conference on Software Engineering, pages 361–370, May 2006.
L. Backstrom and J. Leskovec. Supervised random walks: Predicting and recommending links in social networks. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pages 635–644, February 2011.
K. Balog, L. Azzopardi, and M. de Rijke. Formal models for expert finding in enterprise corpora. In Proceedings of the 29th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, pages 43–50, August 2006.
M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, pages 2399–2434, 2006.
P. Calado, M. Cristo, E.Moura, N. Ziviani, B. Ribeiro-Neto, and M. A. Goncalves. Combining link-based and content-based methods for Web document classification. In Proceedings of the 12th ACM International Conference on Information and Knowledge Management, pages 394–401, November 2003.
E. J. Candes and B. Recht. Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6):717–772, 2009.
H. Deng, I. King, and M. R. Lyu. Formal models for expert finding on DBLP bibliography data. In Proceedings of the IEEE International Conference on Data Management, pages 163–172, December 2008.
H. Ebel, L. I. Mielsch, and S. Bornholdt. Scale-free topology of e-mail networks. Physical Review, E:035103, September 2002.
H. Fang and C. Zhai. Probabilistic models for expert finding. In Proceedings of the 29th European Conference on Information Retrieval, pages 418–430, April 2007.
A. Jamain and D. J. Hand. The naive Bayes mystery: A classification detective story. Pattern Recognition Letters, 26(11):1752–1760, 2005.
T. Joachims. Text categorization with suport vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, pages 137–142, April 1998.
R. H. Keshavan, S. Oh, and A. Montanari. Matrix completion from a few entries. In Proceedings of the 2009 International Symposium on Information Theory, pages 324–328, June 2009.
M. Kim and J. Leskovec. The network completion problem: Inferring missing nodes and edges in networks. In Proceedings of the 2011 SIAM International Conference on Data Mining, April 2011.
Z. Kou and W. Cohen. Stacked graphical models for efficient inference in Markov random fields. In Proceedings of the 2007 SIAM International Conference on Data Mining, April 2007.
Q. Lu and L. Getoor. Link-based text classification. In Proceedings of the IJCAIWorkshop on Text Mining and Link Analysis, August 2003.
J. Neville and D. Jensen. Iterative classification in relational data. In Proceedings of the AAAI Workshop on Statistical Relational Learning, pages 42–49, 2000.
H. H. Permuter, J.M. Francos, and I. Jermyn. A study of Gaussian mixturemodels of color and texture features for image classification and segmentation. Pattern Recognition, 39(4):695–706, 2006.
J. Platt, N. Cristianini, and J. Shawe-Taylor. Large margin DAGs for multiclass classification. In Proceedings of the Fourteenth Neural Information Processing Systems Conference, pages 547–553, December 2000.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3), 2008.
P. Serdyukov, H. Rode, and D. Hiemstra. Modeling multi-step relevance propagation for expert finding. In Proceedings of the 17th ACM International Conference on Information and Knowledge Management, pages 1133–1142, October 2008.
Q. Shao, Y. Chen, S. Tao, X. Yan, and N. Anerousis. Efficient ticket routing by resolution sequence mining. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 605–613, August 2008.
X. Song, B. L. Tseng, C. Y. Lin, and M. T. Sun. ExpertiseNet: Relational and evolutionary expert modeling. In User Modeling, pages 99–108, 2005.
D. Wang, Z. Wen, H. Tong, C. Y. Lin, C. Song, and A. L. Barabasi. Information spreading in context. In Proceedings of the 20th International Conference on the World Wide Web, pages 735–744, March–April 2011.
Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 42–49, August 1999.
Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 412–420, July 1997.
J. Yedidia, W. Freeman, and Y. Weiss. Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory, 51(7):2282–2312, 2005.
J. Yedidia,W. T. Freeman, and Y.Weiss. Generalized belief propagation. In Proceedings of the Fourteenth Neural Information Processing Systems Conference, pages 689–695, December 2000.
H. Zhang. The optimality of naive Bayes. In Proceedings of the 17th International FLAIRS Conference. AIII Press, May 2004.
D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. S. Olkopf. Learning with local and global consistency. In Proceedings of the Eighteenth Neural Information Processing Systems Conference, pages 321–328, December 2004.
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Miao, G., Moser, L.E., Yan, X., Tao, S., Chen, Y., Anerousis, N. (2012). Reliable Ticket Routing in Expert Networks. In: Dai, H., Liu, J., Smirnov, E. (eds) Reliable Knowledge Discovery. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1903-7_7
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DOI: https://doi.org/10.1007/978-1-4614-1903-7_7
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