  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.
