[1]曲媛媛,宫莉莹,贺维.一种RNN-DBN的网络购物风险评估方法[J].哈尔滨理工大学学报,2019,(04):105-109.[doi:10.15938/j.jhust.2019.04.018]
 QU Yuan-yuan,GONG Li-ying,HE Wei.A Method of Online Shopping Risk Assessment Based on RNN-DBN[J].哈尔滨理工大学学报,2019,(04):105-109.[doi:10.15938/j.jhust.2019.04.018]
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一种RNN-DBN的网络购物风险评估方法()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2019年04期
页码:
105-109
栏目:
计算机与控制工程
出版日期:
2019-08-25

文章信息/Info

Title:
A Method of Online Shopping Risk Assessment Based on RNN-DBN
文章编号:
1007-2683(2019)04-0105-05
作者:
曲媛媛12宫莉莹12贺维3
(1.哈尔滨理工大学 计算机科学与技术学院,黑龙江 哈尔滨 150080;
2.黑龙江农业工程职业学院 信息学院,黑龙江 哈尔滨 150088;
3.哈尔滨师范大学 计算机科学与信息工程学院,黑龙江 哈尔滨 150025)
Author(s):
QU Yuan-yuan12GONG Li-ying12HE Wei3
(1.School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China;
2.School of Information, Heilongjiang Agricultural Engineering Vocational College, Harbin 150088, China;?
3.School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China)
关键词:
深度学习循环神经网络深度信念网络风险评估网络购物
Keywords:
deep learning recurrent neural network (RNN) deep belief network (DBN) risk assessment online shopping
分类号:
TP309.2
DOI:
10.15938/j.jhust.2019.04.018
文献标志码:
A
摘要:
针对网络购物过程中的交易风险问题,提出一种利用深度学习技术中的循环神经网络(recurrent neural network, RNN)模型和深度置信网络(deep belief network, DBN)模型来进行网络购物风险评估的方法。该方法首先确定交易风险评估的多个影响因素,然后采用RNN模型对主观因素进行语义分析和情感分类,从而实现定性的主观评价到定量的客观评价的转化,最后采用DBN模型对所有客观影响因素进行交易风险综合评估。通过模拟实验验证,所提出的方法能够有效的解决交易风险评估问题,同时相比传统方法准确性更高,且评价结果更为科学。
Abstract:
Aiming at the problem of transaction risk in online shopping, this paper proposes a method of online shopping risk assessment based on recurrent neural network (RNN) model and deep belief network (DBN) model in deep learning technology. Firstly, the method determines multiple influencing factors of transaction risk assessment. Then we use the RNN model to carry out semantic analysis and sentiment classification of subjective factors so as to realize the transformation from qualitative subjective evaluation to quantitative objective evaluation. Finally, the DBN model is used to analyze all the objective influencing factors conduct a comprehensive assessment of transaction risk. The simulation results show that the proposed method can effectively solve the problem of transaction risk assessment, and at the same time, it has higher accuracy compared with the traditional method, and the evaluation result is more scientific.

参考文献/References:

[1]李林娟.中国网络购物现状与发展趋向分析[J].中国管理信息化,2016,19(20):135.
[2]周欣元.信息安全风险评估综述[J].电子技术与软件工程,2016(20):214.
[3]BOULOIZ H,TKIOUAT M,GALLAB M.Towards a Model for Developing an Information System as a Decision Support to Risk Assessment[J].International Journal of Industrial and Systems,2017,25(1):110.
[4]MILLAN C L,GARZON D,NAVARRO S.C2C Interactions Creating Value in the Route of Santiago [J].Journal of Business Research,2016,69(11):5448.
[5]MIN C G, PARK J K, HUR D,et al.A Risk Evaluation Method for Ramping Capability Shortage in Power Systems [J].Energy,2016,113:1316.
[6]樊雅琴,王炳皓,王伟,等.深度学习国内研究综述[J].中国远程教育,2015,(6):27.
[7]KIM S,PARK B,SONG B S,et al.Deep Belief Network Based Statistical Feature Learning for Fingerprint Liveness Detection [J],Pattern Recognition Letters, 2016 , 77 (S) :58.
[8]胡昕.基于RNN的网络安全态势预测方法[J].现代计算机,2017(6):14.
[9]潘广源,柴伟,乔俊飞.DBN网络的深度确定方法[J].控制与决策,2015(2):256.
[10]孙劲光,蒋金叶,孟祥福,等.一种数值属性的深度置信网络分类方法[J].计算机工程与应用,2014,50(2):112.
[11]彭惠,王永瑞.C2C电子商务市场整体卖家信用分布研究[J].情报杂志,2012,31(5):122.
[12]黎亚雄,张坚,强潘,等.基于RNNRBM语言模型的语音识别研究[J].计算机研究与发展,2014,51(9):1936.
[13]TOBIYAMA S,YAMAGUCHI Y,SHIMADA H,et al.Malware Detection with Deep Neural Network Using Process Behavior [C]//Computer Software and Applications Conference,2016:577.
[14]刘明珠,郑云非,樊金,等.基于深度学习法的视频文本区域定位与识别[J]. 哈尔滨理工大学学报,2016,21(6): 61.
[15]吕淑宝,王明月,翟祥,等.一种深度学习的信息文本分类算法[J]. 哈尔滨理工大学学报,2017, 22 (2):105.
[16]笱程成,秦宇君,田甜,等.一种基于RNN的社交消息爆发预测模型[J].软件学报,2017,28(11):3030.
[17]李飞,高晓光,万开方.基于动态Gibbs采样的RBM训练算法研究[J].自动化学报,2016,42 (6):931.
[18]黎亚雄,张坚强,潘登,等.基于RNNRBM语言模型的语音识别研究[J].计算机研究与发展,2014,51(9):1936.
[19]肖熠,鲁永泉,谢思烨.一种针对GPU资源的深度学习容器云研究[J].中国传媒大学学报,2017(6):16.
[20]陈云,石松,潘彦,等.基于SVM混合集成的信用风险评估模型[J].计算机工程与应用,2016,52 (4):115.

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备注/Memo

备注/Memo:
?收稿日期: 2017-12-27
基金项目:
国家自然科学基金(61773388,61374138)
作者简介:
曲媛媛(1985—),女,硕士,实验员;
宫莉莹(1979—),女,硕士,实验师
通信作者:
贺维(1980—),男,博士,讲师,E-mail: 64282426@qq.com
更新日期/Last Update: 2019-09-04