Huan Sun
ABSTRACT
Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns.
In particular, we observe an interesting phenomenon that an expert tends to transfer a task to someone whose knowledge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based routing pattern is developed. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to combine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accurately predicts their completion time. Under three different quality measures, our method significantly outperforms all the alternatives with more than 75% accuracy gain. In practice, with the help of our model, hypotheses on how to improve a collaborative network can be tested quickly and reliably, thereby significantly easing performance improvement of collaborative networks.
Supplemental Material
- Bugzilla: http://www.bugzilla.org/.Google Scholar
- http://en.wikipedia.org/wiki/ibmwebsphere.Google Scholar
- K. Balog, L. Azzopardi, and M. De Rijke. Formal models for expert finding in enterprise corpora. In SIGIR, pages 43--50. ACM, 2006. Google ScholarDigital Library
- R. Bellman. A markovian decision process. Technical report, DTIC Document, 1957.Google Scholar
- F. Benevenuto, T. Rodrigues, M. Cha, and V. Almeida. Characterizing user behavior in online social networks. In SIGCOMM on IMC, pages 49--62. ACM, 2009. Google ScholarDigital Library
- C. M. Bishop and N. M. Nasrabadi. PRML, volume 1. springer New York, 2006.Google Scholar
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993--1022, 2003. Google ScholarDigital Library
- L. M. Camarinha-Matos and H. Afsarmanesh. A comprehensive modeling framework for collaborative networked organizations. Journal of Intelligent Manufacturing, 18(5):529--542, 2007.Google ScholarCross Ref
- N. Cristianini and J. Shawe-Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press, 2000. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In SIGKDD, pages 137--146. ACM, 2003. Google ScholarDigital Library
- D. Liu and J. Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45(1):503--528, 1989. Google ScholarDigital Library
- G. Miao, L. E. Moser, X. Yan, S. Tao, Y. Chen, and N. Anerousis. Generative models for ticket resolution in expert networks. In SIGKDD, pages 733--742. ACM, 2010. Google ScholarDigital Library
- G. Miao, S. Tao, W. Cheng, R. Moulic, L. E. Moser, D. Lo, and X. Yan. Understanding task-driven information flow in collaborative networks. In WWW, pages 849--858. ACM, 2012. Google ScholarDigital Library
- T. M. Mitchell. Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 45, 1997. Google ScholarDigital Library
- H. Moskowitz and K. Tang. Bayesian variables acceptance-sampling plans: quadratic loss function and step-loss function. Technometrics, 34(3):340--347, 1992. Google ScholarDigital Library
- J. C. Platt, N. Cristianini, and J. Shawe-taylor. Large margin dags for multiclass classification. In NIPS, pages 547--553, 2000.Google Scholar
- P. Serdyukov, H. Rode, and D. Hiemstra. Modeling multi-step relevance propagation for expert finding. In CIKM, pages 1133--1142. ACM, 2008. Google ScholarDigital Library
- Q. Shao, Y. Chen, S. Tao, X. Yan, and N. Anerousis. Efficient ticket routing by resolution sequence mining. In SIGKDD, pages 605--613. ACM, 2008. Google ScholarDigital Library
- D. Wang, Z. Wen, H. Tong, C.-Y. Lin, C. Song, and A.-L. Barabási. Information spreading in context. In WWW, pages 735--744. ACM, 2011. Google ScholarDigital Library
- S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts. Who says what to whom on twitter. In WWW, pages 705--714. ACM, 2011. Google ScholarDigital Library
- Z. Yang, J. Guo, K. Cai, J. Tang, J. Li, L. Zhang, and Z. Su. Understanding retweeting behaviors in social networks. In CIKM, pages 1633--1636. ACM, 2010. Google ScholarDigital Library
- H. Zhang, E. Horvitz, Y. Chen, and D. C. Parkes. Task routing for prediction tasks. In AAMS-Volume 2, pages 889--896. IFAAMS, 2012. Google ScholarDigital Library
- E. Zhong, W. Fan, J. Wang, L. Xiao, and Y. Li. Comsoc: adaptive transfer of user behaviors over composite social network. In SIGKDD, pages 696--704. ACM, 2012. Google ScholarDigital Library
Index Terms
- Analyzing expert behaviors in collaborative networks
Recommendations
Understanding task-driven information flow in collaborative networks
WWW '12: Proceedings of the 21st international conference on World Wide WebCollaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers' problems. In such networks, information flow among members is driven by the ...
Task routing for prediction tasks
AAMAS '12: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2We describe methods for routing a prediction task on a network where each participant can contribute information and route the task onwards. Routing scoring rules bring truthful contribution of information about the task and optimal routing of the task ...
Generative models for ticket resolution in expert networks
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data miningTicket 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 ...
Comments