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.
@article{arxiv.2211.07603,
title = {Problem Classification for Tailored Helpdesk Auto-Replies},
author = {Reece Nicholls and Ryan Fellows and Steve Battle and Hisham Ihshaish},
journal= {arXiv preprint arXiv:2211.07603},
year = {2022}
}
Comments
Artificial Neural Networks and Machine Learning, ICANN 2022, 31st International Conference on Artificial Neural Networks, Bristol, UK, September, 2022, Proceedings; Part IV (445 454)