南京理工大学学报（自然科学版）2019，Vol.43Issue(1)：13-21,47,10.DOI:10.14177/j.cnki.32-1397n.2019.43.01.002
基于注意力卷积神经网络的工作票专家推荐方法
Expert recommendation for trouble tickets using attention-based CNN model
摘要
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 intro-ducing attention.关键词
专家推荐/卷积神经网络/注意力机制/系统运维Key words
expert recommendation/convolutional neural network/attention mechanism/system oper-ation and maintenance分类
信息技术与安全科学引用本文复制引用
何柔萤,徐建..基于注意力卷积神经网络的工作票专家推荐方法[J].南京理工大学学报（自然科学版）,2019,43(1):13-21,47,10.基金项目
国家自然科学基金(61872186,61802205) （61872186,61802205）
江苏省研究生科技与实践创新计划项目(KYCX17_0403) （KYCX17_0403）
