|Table of Contents|

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

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年01期
Page:
13-
Research Field:
Publishing date:

Info

Title:
Expert recommendation for trouble tickets usingattention-based CNN model
Author(s):
He RouyingXu Jian
School of Computer Science and Engineering,Nanjing University of Scienceand Technology,Nanjing 210094,China
Keywords:
expert recommendation convolutional neural network attention mechanism system operation and maintenance
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2019.43.01.002
Abstract:
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.

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Last Update: 2019-02-28