vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff39b62e000000707d100001000500 Assignment of problem tickets to a proper resolver group is an important aspect and crucial step in any IT Service management tools like IT Service desk systems. Manual categorization of tickets may lead to dispatching of problem tickets to an inappropriate expert group, reassignment of tickets, delays the response time and interrupts the normal functioning of the business. Traditional supervised machine learning approaches can be leveraged to train an automated service desk ticket classifier by using the historical ticket data. Sparsity, non-linearity, overfitting and handcrafting of features are some of the issues concerning the traditional ticket classifiers. In this research work, a deep neural network based on Convolution Neural Network (CNN) is proposed for the automated classification of service desk tickets. CNN automatically extracts the most salient features of the ticket descriptions represented using word embeddings. The extracted features are further used by the output classification layer for efficient ticket category prediction. To corroborate the efficacy of the proposed ticket classifier model, we empirically validated it using a real IT infrastructure service desk data and compared the results with the traditional classifier models like Support Vector machines, Naive Bayes, Logistic Regression and K-nearest neighbour. The proposed CNN model with proper hyperparameters tuning outperforms the traditional classifiers in terms of overall model performance. Assignment of tickets to the correct domain groups, speedy resolution, improved productivity, increased customer satisfaction and uninterrupted business are some of the benefits of the proposed automated ticket classifier model.
S.P. Paramesh1, K.S. Shreedhara2
Central University of Karnataka, India1, University B.D.T College of Engineering, India2
Service desk, Machine learning, Deep neural networks, Convolution Neural Network, Word Embeddings
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 13 , Issue: 1 , Pages: 2805 - 2812 )
Date of Publication :
October 2022
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194
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