The ticket automation provides crucial support for the normal operation of IT software systems. An essential task of ticket automation is to assign experts to solve upcoming tickets. However, facing thousands of tickets, inappropriate assignments will make tickets transfer frequently among experts, which causes time delays and wasted resources. Effectively and efficiently finding an appropriate expert in fewer steps is vital to ticket automation. In this paper, we proposed a sequence to sequence based translation model combined with a recurrent recommendation network to recommend appropriate experts for tickets. The sequence to sequence model transforms the ticket description into the corresponding resolution for capturing the potential and useful features of representing tickets. The recurrent recommendation network recommends the appropriate expert based on the assumption that the previous expert in the recommendation sequence cannot solve the expert. To evaluate the performance, we conducted experiments to compare several baselines with SSR-TA on two real-world datasets, and the experimental results show that our proposed model outperforms the baselines. The comparative experiment results also show that SSR-TA has a better performance of expert recommendations for user-generated tickets.

翻译：工单自动化为IT软件系统的正常运行提供了关键支持。工单自动化的核心任务在于指派专家解决即将到来的工单。然而，面对成千上万的工单，不当的指派会导致工单在专家间频繁流转，从而造成时间延迟与资源浪费。如何以更少的步骤高效且准确地找到合适的专家，对工单自动化至关重要。本文提出一种基于序列到序列（seq2seq）的翻译模型，结合循环推荐网络，为工单推荐合适的专家。序列到序列模型将工单描述转换为对应的解决方案，以捕捉表征工单的潜在有用特征；循环推荐网络则基于“推荐序列中的前一位专家无法解决问题”这一假设，推荐合适的专家。为评估性能，我们在两个真实数据集上开展了实验，将SSR-TA与多种基线方法进行对比。实验结果表明，我们提出的模型优于所有基线方法。对比实验结果还显示，SSR-TA在为用户生成的工单推荐专家方面具有更优的性能。