Abstract

Collaborative networks are composed of experts who coop-erate 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 knowl-edge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based rout-ing 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 com-bine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accu-rately predicts their completion time. Under three different quality measures, our method significantly outperforms al-l 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 improve-ment of collaborative networks.