Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs, Simon T; Drieschner, Clemens; Wittges, Holger

doi:10.24251/hicss.2022.238

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Under the topic of the Resolution Time Prediction system, the application of ML plays an essential role in making this system possible and successful [11], [12], [13]. A few ML algorithms are commonly used in the Resolution Time Prediction system, including Decision Tree [5], [14] Random Forest [9], [15] and Gradient Boosting [16].…”

Section: Methodsmentioning

confidence: 99%

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Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Tong-Ern,

Su-Cheng,

Kok-Why

et al. 2024

JOIV : Int. J. Inform. Visualization

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The quality of customer service emphasizes support tickets. An excellent support ticket system qualifies businesses to provide clients with the finest level of customer support. This enables enterprises to guarantee the consistency of quality customer service delivered successfully, ensuring all clients have a good experience regardless of the nature of their inquiry or issue. To further achieve a higher efficiency of resource allocation, this is when the prediction of ticket resolution time comes into place. The advancing technologies, including artificial intelligence (AI) and machine learning (ML), can perform predictions on the duration required to tackle specific problems based on past similar data. ML enables the possibility of automatically classifying tickets, making it possible to anticipate the time resolution for cases. This paper explores various ML techniques widely applied in the Resolution Time Prediction system and investigates the performance of three selected ML techniques via the benchmarking dataset obtained from the UCI Machine Learning Repository. Implementing selected techniques will involve creating a graphical user interface and data visualization to provide insight for data analysis. The best technique will be concluded after performing the ML technique evaluation. The evaluation metrics involved in this step include Root Mean Square Error (RMSE) and Root Mean Absolute Error (MAE). The experimental evaluation shows that the best performance among the selected ML techniques is Random Forest (RF).

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Section: Methodsmentioning

confidence: 99%

“…A predictive model that utilizes ML techniques and past data's underlying pattern to anticipate ticket resolution times will be the ideal solution for quickening ticket assignment and completion [9], [10]. After the system receives a new ticket, the trained ML model will auto-calculate the time required to resolve this query.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Tong-Ern,

Su-Cheng,

Kok-Why

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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“…Use of ML via NLP to classify textual data into predetermined labels (using supervised ML) has also evolved significantly in the last decade. Fuchs, Drieschner, and Wittges 2022 have reviewed the recent literature at the intersection of these two areas. We will only discuss a few recent papers, typically spanning three scopes: …”

Section: Related Workmentioning

confidence: 99%

Deploying automated ticket router across the enterprise

Ackerman

Alexander²,

Bennett

et al. 2023

AI Magazine

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With the recent advances in machine learning, the use of natural language processing (NLP) technology to support various business processes has been increasing. This paper discusses the use of NLP to route more than one million live client tickets annually to the appropriate service personnel in 67 support missions across IBM. Each mission supports a product family with multiple support teams, each requiring different skills for the engineers. We discuss three important aspects of such a large‐scale deployment: (i) The use of a centralized team with a common machine learning infrastructure and practices to support the entire enterprise. (ii) The processes and quality of such a deployment from the perspective of one support mission, namely, IBM's z/OS family. (iii) Careful monitoring of the deployed models to detect drifts in the routing behavior. Despite vast differences in the technical contents of the support missions, it is possible to define common processes and metrics across the enterprise, without requiring a dedicated machine learning team for each mission. In addition, we provide examples of the business policies and metrics from the perspective of the z/OS mission to demonstrate the utility of the approach and the outcome.

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“…Harun et al mentioned that the strategy for constructing an automated service desk ticket classifier system was created by Paramesh et al [9], who did their research by analyzing data from IT infrastructure helpdesks. Traditional supervised machine-learning methods are used to construct classification models [10]. A comprehensive investigation is carried out into the methods that can be used to deal with undesirable, imbalanced, or wrongly labeled data.…”

Section: Related Workmentioning

confidence: 99%

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

Deshai

Rao

Chittineni

et al. 2023

IJ-AI

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In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.

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Improving Support Ticket Systems Using Machine Learning: A Literature Review

Cited by 7 publications

References 44 publications

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Deploying automated ticket router across the enterprise

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

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