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

TL;DR

This paper introduces SSR-TA, a sequence-to-sequence and recurrent network model for expert recommendation in ticket automation, improving accuracy and efficiency in assigning IT support tickets.

Contribution

The paper proposes a novel sequence-to-sequence translation model combined with a recurrent recommendation network for expert assignment, outperforming existing baselines.

Findings

SSR-TA outperforms baseline models in expert recommendation accuracy.

The model effectively captures ticket features through sequence-to-sequence translation.

Experimental results demonstrate improved recommendation performance on real-world datasets.

Abstract

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.