Problem classification for tailored help desk auto replies

Nicholls, Reece; Fellows, Ryan; Battle, Steve; Ihshaish, Hisham

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK
• Start Date: Sep 6, 2022
• End Date: Sep 9, 2022
• Acceptance Date: Jul 12, 2022
• Online Publication Date: Sep 7, 2022
• Publication Date: Sep 7, 2022
• Deposit Date: Sep 23, 2022
• Publicly Available Date: Sep 8, 2024
• Publisher: Springer Verlag
• Volume: 13532 LNCS
• Pages: 445-454
• Series Title: Lecture Notes in Computer Science (LNCS, volume 13532)
• Series Number: 13532
• Series ISSN: 1611-3349; 0302-9743
• Edition: Vol 13532
• Book Title: Artificial Neural Networks and Machine Learning – ICANN 2022
• Chapter Number: 37
• ISBN: 9783031159367
• DOI: https://doi.org/10.1007/978-3-031-15937-4_37
• Keywords: neural networks, data augmentation, helpdesk, supervised learning
• Public URL: https://uwe-repository.worktribe.com/output/9953757
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-031-15937-4_37
• Related Public URLs: https://link.springer.com/book/10.1007/978-3-031-15937-4; ; https://www.springer.com/series/558