A multiapproach generalized framework for automated solution suggestion of support tickets

Abstract

Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end‐to‐end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End‐to‐End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end‐to‐end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real‐world IBM ticket data set. Overall, we observed that end‐to‐end model outperformed similarity search‐based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.

AI for Information Technology Operation (AIOps): A Review of IT Incident Risk Prediction

“…In 2010, the surge of digitization in this area was at its peak, but immediately afterward, showed a sharp decline contributed by the fear of project failure [14]. However, there is a steady interest observed from 2013, when ITSM started to integrate Machine learning (ML), Deep Learning (DL), and AIOPS, which reinstated investors' confidence in this industry [15].…”

Multiple Severity-Level Classifications for IT Incident Risk Prediction

“…Currently, there are limited studies on text classification for automated incident labeling [7]. Most of the existing studies focused on the implementation of conventional ML models, and Natural Language Processing (NLP) techniques (for pre-processing and feature extraction) [3], [4], [7]. Subsequently, there are fewer studies dedicated to implementing DL to automate IT incident prediction.…”

“…Subsequently, there are fewer studies dedicated to implementing DL to automate IT incident prediction. At the same time, the adoption of state-of-the-art AI techniques has been shown to outperform conventional approaches in other related fields [7]- [9]. Therefore, we foresee an opportunity to exploit these techniques to enable automation as an alternative to the manual approaches currently adopted in the industry.…”

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

“…We can use a variety of machine-learning approaches to build an automatic ticket classification system that addresses all of these issues. For example, to categorize a service desk ticket, an automated ticket classifier analyses the ticket's enddescription user in natural language, which uses both supervised and unsupervised machine learning approaches to build ticket classifier models [7]. Furthermore, classifier models can be constructed using supervised machine learning techniques such as classification algorithms when the label or category of historical ticket data is known [8].…”