Recommending resolutions of ITIL services tickets using Deep Neural Network

Application development and maintenance is a good example of Information Technology Infrastructure Library (ITIL) services in which a sizable volume of tickets are raised everyday for different issues to be resolved in order to deliver uninterrupted service. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. It will be beneficial to automatically extract information from the description of tickets to improve operations like identifying critical and frequent issues, grouping of tickets based on textual content, suggesting remedial measures for them etc. In particular, the maintenance people can save a lot of effort and time if they have access to past remedial actions for similar kind of tickets raised earlier based on history data. In this work we propose an automated method based on deep neural networks for recommending resolutions for incoming tickets. We use ideas from deep structured semantic models (DSSM) for web search for such resolution recovery. We project a small subset of existing tickets in pairs and an incoming ticket to a low dimensional feature space, following which we compute the similarity of an existing ticket with the new ticket. We select the pair of tickets which has the maximum similarity with the incoming ticket and publish both of its resolutions as the suggested resolutions for the latter ticket. The experiment of our data sets shows that we are able to achieve a promising similarity match of about 70% - 90% between the suggestions and the actual resolution.