摘要
为研究眼动行为数据挖掘在提取网上购物决策因子中的应用,首先通过客户体验管理(Customer Experience Management,CEM)对客户网上购物决策因子进行初步分析和过滤;然后根据大量客户网上购物时的人机交互行为数据,应用马尔科夫链算法预测其相关行为,并结合行为分析进一步得到其重要影响因子;最后通过眼动行为数据挖掘,确认影响网上购物的主决策因子,并获取各主决策因子的权重.实验证明由眼动分析获取的网上购物主决策因子及其权重更加有效.
To study the application of eye movement behavior data mining in identifying decision factors on online shopping, the preliminary analysis and filtering of decision factors on online shopping are made by Customer Experience Management （CEM）. Then, based on a large amount of online shoppers＇ hu- man-computer interactive behavior data, the behaviors related with online shopping are predicted using Markov chain algorithm, and then the more important decision factors are further identified by analyzing the behaviors. Finally, through eye movement behavior data mining, the main decision factors on online shopping are confirmed and their weights are obtained. Experiments show that the main decision factors on online shopping and their weights obtained by the eye movement analysis are more effective.
出处
《上海海事大学学报》
北大核心
2014年第1期60-64,共5页
Journal of Shanghai Maritime University
基金
国家自然科学基金(71201099)
上海市浦江人才计划(13PJC066)
上海市教育委员会科研创新项目(14YZ111)
关键词
客户体验管理
马尔科夫链
眼动行为
决策因子
customer experience management
Markov chain
eye movement behavior
decision factor
