DeepRouting: A Deep Neural Network Approach for Ticket Routing in Expert Network

Han, Jianglei; Sun, Aixin

doi:10.1109/scc49832.2020.00057

Cited by 6 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, ML algorithms like SVM and RF have proven to be more accurate and precise than "older" rules-based approaches or Decision Trees [2,8,23]. Also, there is some evidence that DNNs could outperform the present best-performing algorithms SVM and RF, especially in the case of large training data and many classes to classify tickets in [21,29]. SVM and RF can therefore be considered standard approach algorithms for support ticket classification.…”

Section: Discussionmentioning

confidence: 99%

“…When analyzing the 4 papers presenting DNNbased approaches [19,21,23,29], we found that two of those papers used specific LSTM-networks, which were both the best performing approaches in that papers [19,29]. Since two papers are not a sufficiently large sample, no general statement can be derived here.…”

Section: <60% Accuracymentioning

confidence: 94%

“…Han and Sun [21] x Mour, Dey [22] x x Revina, Buza [23] x Xu, Mu [24] x x Al-Hawari and Barham [8] x…”

Section: Sent Pred Othermentioning

confidence: 99%

“…This probably is the reason that mostly in the years 2018 and 2019, RF and SVM were the approaches most implemented and evaluated and also best performing [2,6,8,9,30,36]. Since 2019, some authors also propose artificial-neuronal-networkbased solutions, because of the good performance of neuronal network approaches in other fields of ML [19,21,23,29]. While there is some promising evidence, that Deep Neuronal Network (DNN) based solutions might be able to outperform present ML approaches like RF or SVM [29] (reported accuracies: 77% [29], 83% [19], 99% [21]), this point seems not to be fully reached yet [23].…”

Section: <60% Accuracymentioning

confidence: 99%

“…Since 2019, some authors also propose artificial-neuronal-networkbased solutions, because of the good performance of neuronal network approaches in other fields of ML [19,21,23,29]. While there is some promising evidence, that Deep Neuronal Network (DNN) based solutions might be able to outperform present ML approaches like RF or SVM [29] (reported accuracies: 77% [29], 83% [19], 99% [21]), this point seems not to be fully reached yet [23]. Revina, Buza [23] find in their conclusion (page 11) "that simple algorithms work well if using appropriate linguistic features" and can be equally performative.…”

Section: <60% Accuracymentioning

confidence: 99%

See 4 more Smart Citations

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: <60% Accuracymentioning

confidence: 94%

“…Han and Sun [21] x Mour, Dey [22] x x Revina, Buza [23] x Xu, Mu [24] x x Al-Hawari and Barham [8] x…”

Section: Sent Pred Othermentioning

confidence: 99%

Section: <60% Accuracymentioning

confidence: 99%

Section: <60% Accuracymentioning

confidence: 99%

See 3 more Smart Citations

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

Deploying automated ticket router across the enterprise

Ackerman

Alexander²,

Bennett

et al. 2023

AI Magazine

View full text Add to dashboard Cite

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.

show abstract