[1]薛佳[][][],栗觅[][][],马理旺[][][],等. 基于视觉行为的网上用户识别[J].计算机技术与发展,2017,27(02):11-14.
 XUE Jia[][][],LI Mi[][][],MA Li-wang[][][],et al. Online User Identification Based on Visual Behaviors[J].,2017,27(02):11-14.
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 基于视觉行为的网上用户识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年02期
页码:
11-14
栏目:
智能、算法、系统工程
出版日期:
2017-02-10

文章信息/Info

Title:
 Online User Identification Based on Visual Behaviors
文章编号:
1673-629X(2017)02-0011-04
作者:
 薛佳[1][2][3]栗觅[1][2][3]马理旺[1][2][3]吕胜富[1][2][3] 钟宁[1][2][3][4]
 1.北京工业大学 电子信息与控制工程学院 国际WIC研究院;2.脑信息智慧服务北京市国际合作基地;3.磁共振成像脑信息学北京市重点实验室;4.日本前桥工科大学生命信息系,日本前桥371-0816
Author(s):
XUE Jia[1][2][3]LI Mi[1][2][3]MA Li-wang[1][2][3]LYU Sheng-fu[1][2][3]ZHONG Ning[1][2][3][4]
关键词:
 眼动数据用户识别隐马尔可夫模型遗传算法人机交互
Keywords:
 eye movement datauser identificationHMMGAhuman-computer interaction
分类号:
TP391.9
文献标志码:
A
摘要:
 为了解决网上用户的识别问题,研究了基于眼动的隐马尔可夫模型(HMM)的用户识别方法.使用眼动装置获取用户网上行为的眼动数据,并提取显著性眼动特征.使用隐马尔可夫模型分别对不同类型用户建立用户模型,并将用户数据输入模型.利用最大概率原则输出用户类型,并使用优化算法-遗传算法(GA)对HMM进行参数优化,提高了识别准确率.实验结果表明,通过该方法识别网上用户类型是可行的.该研究进一步表明,根据用户的网上行为特点,优化网页结构,能够满足不同用户的个性化需求,还可以对用户的个体行为进行独立挖掘,提高人机交互水平.
Abstract:
 To solve the problem of Web user identification,the method using Hidden Markov Model (HMM) is explored dealing with the data of eye movement.The eye movement data of users’ online behavior are acquired and the significant features of eye movement are extracted.Then The models of different types of users are established using HMM,inputting the data of users into model.The maximum probability principle is applied to output the user type and the Genetic Algorithm (GA) is used to optimize the parameter of HMM,improving the accuracy of identification.The experimental results indicate that the Web users can be recognized effectively by the HMM method.In addition,according to the accurate user identification based on characteristics of user’s online behavior,the structure of web pages can be optimized to meet the need of different users.It also can make the user’s individual behavior independently and improve the level of human-computer interaction.

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更新日期/Last Update: 2017-05-11