Problem Classification for Tailored Helpdesk Auto-Replies
This addresses the need for more relevant automated responses in IT helpdesks, though it is an incremental improvement as it serves as a practical stop-gap rather than a replacement for human agents.
The paper tackled the problem of improving IT helpdesk auto-replies by tailoring content to user queries, using a neural network trained on email data to classify problems and increase relevance.
IT helpdesks are charged with the task of responding quickly to user queries. To give the user confidence that their query matters, the helpdesk will auto-reply to the user with confirmation that their query has been received and logged. This auto-reply may include generic `boiler-plate' text that addresses common problems of the day, with relevant information and links. The approach explored here is to tailor the content of the auto-reply to the user's problem, so as to increase the relevance of the information included. Problem classification is achieved by training a neural network on a suitable corpus of IT helpdesk email data. While this is no substitute for follow-up by helpdesk agents, the aim is that this system will provide a practical stop-gap.