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Expert recommendation for trouble tickets usingattention-based CNN model(PDF)


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Expert recommendation for trouble tickets usingattention-based CNN model
He RouyingXu Jian
School of Computer Science and Engineering,Nanjing University of Scienceand Technology,Nanjing 210094,China
expert recommendation convolutional neural network attention mechanism system operation and maintenance
In order to improve the accuracy of recommending trouble tickets to experts with problem-solving ability,the expert recommendation algorithm based on deep learning are studied by learning the historical trouble ticket data. According to the expert’s professional proficiency level and domain knowledge,an expert’s ability model is constructed,and an expert recommendation framework based on convolution neural network is defined. Attention mechanism is introduced into input layer of the model to enhance the ability of describing the feature extraction of tickets. This paper measures the similarity match score between the problem description and the expert’s model to realize expert recommendation based on quality. Experimental results on real ticket datasets show that the proposed method can improve the accuracy by about 6% compared with the traditional machine learning classification recommendation methods,and can effectively learn the weight of ticket feature by introducing attention.


[1] Li T,Peng W,Perng C,et al. An integrated data-driven framework for computing system management[J]. IEEE Transactions on Systems Man & Cybernetics Part A Systems & Humans,2010,40(1):90-99.
[2]Xu J,Tang L,Li T. System situation ticket identification using SVMs ensemble[J]. Expert Systems with Applications,2016,60:130-140.
[3]Zeng C,Zhou W,Li T,et al. Knowledge guided hierarchical multi-label classification over ticket data[J]. IEEE Transactions on Network & Service Management,2017,14(2):246-260.
[4]Sun H,Srivatsa M,Tan S,et al. Analyzing expert behaviors in collaborative networks[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York,USA:ACM,2014:1486-1495.
[5]李颖,朱保平. 基于交互链路的相似用户推荐算法[J]. 南京理工大学学报,2018,42(2):183-190.
Li Ying,Zhu Baoping. Similar user recommendation algorithm based on interactive link[J]. Journal of Nanjing University of Science and Technology,2018,42(2):183-190.
[6]Shao Q,Chen Y,Tao S,et al. Efficient ticket routing by resolution sequence mining[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York,USA:ACM,2008:605-613.
[7]Xu J,He R,Zhou W,et al. Trouble ticket routing models and their applications[J]. IEEE Transactions on Network & Service Management,2018,15(2):530-543.
[8]李贤慧,余正涛,魏斯超,等. 基于Listwise的深度学习专家排序方法[J]. 模式识别与人工智能,2015,28(11):976-982.
Li Xianhui,Yu Zhengtao,Wei Sichao. Deep learning expert ranking method based on Listwise[J]. Pattern Recognition and Artificial Intelligence,2015,28(11):976-982.
[9]梁斌,刘全,徐进,等. 基于多注意力卷积神经网络的特定目标情感分析[J]. 计算机研究与发展,2017,54(8):1724-1735.
Liang Bin,Liu Quan,Xu Jin,et al. Aspect-based sentiment analysis based on multi-attention CNN[J]. Journal of Computer Research and Development,2017,54(8):1724-1735.

[10] Zhou W,Xue W,Baral R,et al. STAR:a system for ticket analysis and resolution[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York,USA:ACM,2017:2181-2190.
[11]Bahdanau D,Cho K,Bengio Y. Neural machine translation by jointly learning to align and translate[J]. Proceedings of ICLR,2015:1-15.
[12]Yin W,Schütze H,Xiang B,et al. ABCNN:attention-based convolutional neural network for modeling sentence pairs[J]. Computer Science,2015.2015,arXiv:1512.05193.
[13]Wang Lin,Cao Zhu,de Melo G,et al. Relation classification via multi-level attention cnns[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,PA:ACL,2016:1298 1307.
[14]Momtazi S,Naumann F. Topic modeling for expert finding using latent Dirichlet allocation[J]. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery,2013,3(5):346-353.


Last Update: 2019-02-28