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The ticket automation provides crucial support for the normal operation of ITsoftware systems. An essential task of ticket automation is to assign expertsto solve upcoming tickets. However, facing thousands of tickets, inappropriateassignments will make tickets transfer frequently among experts, which causestime delays and wasted resources. Effectively and efficiently finding anappropriate expert in fewer steps is vital to ticket automation. In this paper,we proposed a sequence to sequence based translation model combined with arecurrent recommendation network to recommend appropriate experts for tickets.The sequence to sequence model transforms the ticket description into thecorresponding resolution for capturing the potential and useful features ofrepresenting tickets. The recurrent recommendation network recommends theappropriate expert based on the assumption that the previous expert in therecommendation sequence cannot solve the expert. To evaluate the performance,we conducted experiments to compare several baselines with SSR-TA on tworeal-world datasets, and the experimental results show that our proposed modeloutperforms the baselines. The comparative experiment results also show thatSSR-TA has a better performance of expert recommendations for user-generatedtickets.

SSR-TA: Sequence to Sequence based expert recurrent recommendation for ticket automation