Problem Classification for Tailored Helpdesk Auto-Replies

TL;DR

This paper presents a neural network-based system for classifying IT helpdesk queries to generate tailored auto-replies, aiming to improve relevance and user confidence in automated responses.

Contribution

It introduces a novel application of neural networks for classifying helpdesk problems to personalize auto-replies, enhancing existing automated support systems.

Findings

Neural network effectively classifies helpdesk queries.

Tailored auto-replies increase user confidence.

System provides a practical stop-gap for helpdesk automation.

Abstract

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.